Top 10 AI Programming Languages in 2025

Every search for the top AI programming languages points to Python. But what programming language is used for AI and works better for specific applications?

Relying only on the most popular choice can be limiting. This is especially true when you need high performance, statistical depth, or deployment on a large business scale.

Discover when languages like C++, R, or Julia are better choices. Learn why top tech firms also consider them the best programming languages for AI development.

1. Python

Python is a high-level, general-purpose programming language. It is designed for developer productivity and code readability. Its clean syntax has led to its popularity in web development, task automation, and software development.

Python is one of the top AI programming languages, famously used for the recommendation algorithms in applications like Spotify and Netflix. It is also used for the backend systems of platforms like Instagram and Dropbox.

How Python is Used in AI

• Python is the leading language for data science, machine learning, and deep learning.
• This AI coding language supports the whole machine learning workflow, from preparing data to deploying the model.
• The language is dominant in NLP and Computer Vision. It is used for tasks like sentiment analysis and facial recognition.
• Its ecosystem is central to the growth of generative AI. It simplifies making applications that use Large Language Models (LLMs).

Top Python AI Packages and Libraries

1. NumPy: This is a foundational package for scientific computing. It supplies powerful N-dimensional arrays and mathematical functions.
   
2. Pandas: It gives high-performance DataFrames. This makes it the industry standard for data cleaning, transformation, and analysis.
   
3. Scikit-learn: This is the top choice for classic machine learning. It gives strong tools for classification, regression, clustering, and more.
   
4. TensorFlow: This is Google's end-to-end deep learning platform. It is known for its ability to scale and its production-ready deployment features.
   
5. PyTorch: This is Meta's flexible deep learning library. It is favored in research for its intuitive design and easy debugging with dynamic graphs.

2. R

R is a free, open-source programming language. This AI coding language is also a software environment made specifically for statistical computing and graphics.

Unlike general-purpose languages, its core identity is that of a specialized tool. It was created to help with in-depth data exploration and sophisticated statistical modeling. 

R is also a top programming language for AI that helps create publication-quality data visualizations. R is one of the preferred choices of AI programming languages for projects where statistical accuracy and model clarity are most important.

How R is Used in AI

• R connects traditional statistical modeling with modern machine learning methods.
• It is the standard in academia and in industries like healthcare and FinTech. These fields require deep statistical inference.
• This language is widely used for predictive modeling in AI. This programming language for AI has algorithms for regression, classification, and clustering.
• Its collection of tools is expanding beyond statistics to wider AI applications, including computer vision and NLP.

Top R AI Packages and Libraries

1. Tidyverse: This is a collection of data science packages with a shared design philosophy. It includes core tools like ggplot2 and dplyr.
   
2. ggplot2: This is a declarative data visualization package based on the "Grammar of Graphics." It is considered a standard for creating complex plots.
   
3. caret: This is a complete framework. It unifies the process for training and checking hundreds of machine learning models.
   
4. mlr3: This is a modern, object-oriented machine learning framework. It is the successor to caret and gives greater flexibility and scalability.

3. Julia

Julia is a modern, high-level programming language. This AI coding language delivers high performance and is dynamic. Julia was designed to solve the "two-language problem" in scientific computing.

This problem describes the inefficient workflow of first prototyping in a slow language like Python and then rewriting it in a fast one like C. 

Julia's main feature is giving the development speed of a dynamic language with the raw execution speed of a compiled one, all in a single environment.

How Julia is Used in AI

• Julia leads the niche of Scientific Machine Learning (SciML). This field combines AI models with scientific simulations.
• This top AI programming language excels in applications that need extreme computational performance. Examples include climate modeling and fluid dynamics.
• The language is used in quantitative finance. It is applied to advanced risk analysis and high-performance trading bots.
• Pharmaceutical companies like Pfizer and AstraZeneca use it to speed up drug discovery simulations.

Top Julia AI Packages and Libraries

1. Flux.jl: This is a flexible and elegant machine learning library. It makes use of Julia's automatic differentiation abilities.
   
2. MLJ.jl: This is a unified machine learning framework, similar to Python's scikit-learn. It standardizes the ML workflow in Julia.
   
3. DifferentialEquations.jl: This is a best-in-class suite for solving differential equations. It forms a core part of the SciML ecosystem.
   
4. Turing.jl: This is a universal probabilistic programming language. It is used for Bayesian inference and complex probabilistic modeling.

4. Java

Java is a high-level, object-oriented programming language. It is famous for its "Write Once, Run Anywhere" philosophy, which is made possible by the Java Virtual Machine (JVM).

For decades, it has been the workhorse of corporate IT. Over 90% of Fortune 500 companies use it to build scalable and mission-critical backend systems. This top AI programming language creates applications that cover large-scale web services, big data technologies, and the Android mobile operating system.

How Java is Used in AI

• Java's main AI strength is deploying and scaling models within large-scale business environments.
• This AI programming language puts AI into massive, business-essential codebases where rewriting is not practical.
• Core big data frameworks like Hadoop and Spark run on the JVM. This makes Java important for data pipelines.
• It is used to build scalable and secure server systems for fraud detection and conversational AI.

Top Java AI Packages and Libraries

1. Deeplearning4j (DL4J): This is a commercial-grade, distributed deep learning library for the JVM. It works with Spark and Hadoop.
   
2. Weka: This is a full collection of machine learning algorithms for data mining. It is known for its user-friendly graphical interface.
   
3. Apache Spark MLlib: This is Spark's scalable machine learning library. It gives Java APIs for distributed ML algorithms.
   
4. LangChain4j: This is a Java version of the LangChain framework. It is for building context-aware applications by chaining LLMs with APIs.

5. C++

C++ is a sophisticated, general-purpose programming language. It is defined by its attention to performance, efficiency, and low-level control. Its main identity is that of a systems programming language. This AI coding language is used to build foundational, performance-heavy software like operating systems, game engines, and browsers.

As a compiled language, its code is translated directly into native machine code. This makes it a programming language for AI that is much faster than interpreted languages like Python.

How C++ is Used in AI

• Its most important role is building the high-performance backends of frameworks like TensorFlow and PyTorch.
• C++ is an AI programming language that handles computationally intensive tasks. These include matrix math and parallel processing on CPUs and GPUs.
• C++ is essential for applications where low latency is key. Examples are autonomous vehicles, high-frequency trading, and robotics.
• It is the main language for "embodied AI" systems like robots and drones that need direct hardware control.

Top C++ AI Packages and Libraries

1. TensorFlow C++ API: This allows for direct, high-performance interaction with the TensorFlow core engine, bypassing Python.
   
2. PyTorch C++ API (LibTorch): This makes possible the high-performance deployment of PyTorch models in production environments without a Python dependency.
   
3. dlib: This is a modern and versatile C++ toolkit. It has a wide range of ML algorithms, especially for image processing.
   
4. mlpack: This is a C++ machine learning library with an emphasis on speed and scalability. It has a simple and consistent API.

6. Scala

Scala is a modern, general-purpose language on the JVM. It merges object-oriented and functional programming styles.

It is designed to be concise and type-safe. This allows developers to use powerful functional concepts like immutability. It also retains full, smooth interoperability with the vast collection of Java libraries. Scala’ defining quality, making it one of the top AI programming languages, is its hybrid nature.

How Scala is Used in AI

• Scala's use as an AI programming language is tied to Apache Spark. Spark is the leading framework for large-scale data processing.
  
• As Spark's native language, Scala presents the most performant and complete API for the framework.
  
• It is the top AI programming language for building high-throughput data pipelines. These pipelines prepare massive datasets for AI.
  
• Using Spark MLlib, Scala is used to train machine learning models on distributed data clusters.

Top Scala AI Packages and Libraries

1. Apache Spark: This is the core framework for large-scale, distributed data processing, for which Scala is the native language.
   
2. Spark MLlib: This is Spark's built-in library for scalable machine learning. It has distributed algorithms for common tasks.
   
3. Akka: This is a toolkit for building highly concurrent and resilient applications. It is often used for the real-time serving layer of AI systems.
   
4. Deeplearning4j (DL4J): This is a major JVM deep learning library that is fully interoperable with Scala. It can run on top of Spark.

7. Javascript

JavaScript is a high-level scripting language. With HTML and CSS, it forms the foundation of the World Wide Web.

One of the top AI programming languages to work with, JavaScript’s main purpose is client-side scripting.

This coding language runs directly in a user's web browser to make pages interactive and dynamic. The Node.js runtime has evolved into a full-stack language. This programming language for AI is capable of handling both frontend and backend development.

How JavaScript is Used in AI

• JavaScript's main role is to deploy AI models to run directly in a user's web browser.
• This allows for real-time, interactive applications like webcam-based gesture control or virtual backgrounds.
• Running models in the browser improves user privacy by keeping sensitive data on the user's device.
• This programming language for AI lowers server costs and removes network latency by offloading computation to the user.

Top JavaScript AI Packages and Libraries

1. TensorFlow.js: This is the main library for ML in JavaScript. It allows model training and execution in the browser with GPU acceleration.
   
2. Transformers.js: This is a Hugging Face library. It runs state-of-the-art transformer models like GPT-2 directly in the browser.
   
3. LangChain.js: This is the JavaScript version of LangChain. It gives tools to build complex applications and agents powered by LLMs.
   
4. MediaPipe.js: This is a Google library for high-performance, real-time media processing pipelines, such as face and pose detection.

8. Lisp

Lisp (LISt Processing) is one of the oldest high-level programming languages. This one of the oldest of the top AI programming languages, designed from its start in 1958 specifically for artificial intelligence research.

This AI coding language’s influence is immense. It pioneered concepts that are now standard in modern programming.

These include tree data structures, garbage collection, and the interactive REPL. Its most powerful feature is homoiconicity. This is the principle that a program's source code is a basic data structure of the language itself.

How Lisp is Used in AI

• Lisp is the historical premier language for Symbolic AI, which works on manipulating symbols and logical rules.
• Its list-based structure is ideal for representing complex knowledge structures and logical expressions.
• Though its dominance has faded, it is one of the best AI programming languages used for expert systems and for prototyping new AI algorithms.
• The emerging field of neuro-symbolic AI could lead to a renewed interest in Lisp's principles.

Top Lisp AI Packages and Environments

1. Common Lisp: This is a powerful, industrial-strength Lisp dialect. It is standardized for building large, dependable applications.
   
2. Scheme: This is a minimalist Lisp dialect. It is known for its elegant design and its use of functional programming principles.
   
3. SBCL (Steel Bank Common Lisp): This is a high-performance, open-source compiler for Common Lisp. It is known for generating fast native code.
   
4. Emacs + SLIME: The combination of the Emacs editor and the SLIME plugin creates the standard, highly interactive IDE for Common Lisp.

9. Haskell

Haskell is a high-level language top programming language for artificial intelligence. It is defined by its strict following of the purely functional programming paradigm.

Haskell is one of the AI programming languages that are statically typed. Its core design principles are immutability (data cannot be changed) and purity (functions have no side effects). These are managed through its powerful type system. 

This attention to correctness and reliability allows the compiler to catch a wide range of errors before a program is ever run.

How Haskell is Used in AI

• Haskell occupies a niche in AI areas where formal correctness and reliability are the highest priorities.
• Haskell is an AI coding language used in high-assurance and safety-focused domains like finance, aerospace, and defense.
• This AI programming language has an expressive type system that is excellent for translating complex mathematical algorithms into correct code.
• The top AI programming language is heavily used in academic research on creating verifiably safe and correct AI systems.

Top Haskell AI Packages and Libraries

1. HaskTorch: This is a Haskell interface to PyTorch's C++ API. It combines PyTorch's power with Haskell's type safety.
   
2. HLearn: This is a classical machine learning library. It uses abstract algebra to create expressive and composable models.
   
3. HMatrix: This is a foundational library for numerical computing in Haskell. It gives support for matrices, vectors, and linear algebra.
   
4. GHC (Glasgow Haskell Compiler): This is the main compiler for Haskell. It features a world-class optimizing backend and advanced language features.

10. Prolog

Prolog (Programming in Logic) is a declarative, logic-based language. It is fundamentally different from mainstream imperative languages and most AI programming languages, for that matter.

Instead of specifying how to solve a problem, the programmer describes the problem area.

This is done by defining a knowledge base of facts and rules. This programming language for AI uses its own execution engine, then uses this knowledge base to logically deduce a solution to a query.

How Prolog is Used in AI

• Like Lisp, Prolog is a foundational language of Symbolic AI. It is used for logical reasoning and knowledge manipulation.
• Its most powerful feature is a built-in inference engine. This engine automatically explores solutions by backtracking.
• It is a natural fit for expert systems, where human knowledge is captured as a series of logical rules.
• Prolog's inherent explainability makes it a great fit for Explainable AI (XAI) as a "reasoning layer."

Top Prolog AI Packages and Environments

1. SWI-Prolog: This is a popular, feature-rich, and actively developed open-source Prolog environment with extensive libraries.
   
2. GNU Prolog: This is a free, ISO-standard Prolog compiler that aims for performance by compiling to native code.
   
3. Constraint Logic Programming (CLP): This is an extension of logic programming. It combines Prolog with efficient constraint satisfaction for optimization.
   
4. Logtalk: This is an object-oriented logic programming language. It runs on most Prolog environments as a backend.

Choosing the Best AI Programming Language For Your AI Platform

Picking the best AI programming language for you depends entirely on your use case.

We did our best to show you the theory behind the top AI programming languages. But theory alone builds nothing.

At Entrans, we launched our AI agent Thunai in under 4 months, from proof of concept to having paid users. This is an impressive benchmark for any development agency, but it does mean you need to work with experts.

Work with the Team Entans to get started with an AI app that you can go-to-market with in record time.

Book a free consultation call to know how we can best help you!

FAQs in AI Programming Languages

What programming language is best for AI?

Python is the most popular and widely used language for AI. This is because of its simple syntax and extensive libraries. However, other languages like R, C++, and Java are also used for specialized tasks in statistics, performance-heavy operations, and large-scale business systems.

Is AI coded in Python?

A large part of AI development, especially in machine learning and data science, is done in Python. Its powerful libraries like TensorFlow and PyTorch make it easy to build and train complex models. However, the high-performance core of these libraries is often written in C++, and other languages are also used.

Is C++ or Python better for AI?

Neither is strictly better, as they serve different purposes. They are often used together. Python is superior for rapid prototyping, research, and ease of use. This makes it ideal for most AI development tasks. C++ is the preferred choice for applications where speed is a top priority and where low-level memory control is needed.

Is Python or Java better for AI?

Python is overwhelmingly preferred for model development, research, and data science. This is due to its simplicity and powerful, specialized AI libraries. Java's strength is in adding pre-trained AI models into large, existing business applications and building scalable, enterprise-grade systems. So, Python is generally better for creating the AI itself, while Java is often used for deploying it.