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Artificial intelligence (AI) is changing how we work, solve problems, and develop new technologies, making the choice of an AI programming language crucial. In 2025, AI tools and applications are more popular than ever, and the right coding language can significantly enhance your ability to build smarter, faster systems.
AI programming languages are essential tools that help developers create software that mimics human intelligence. Languages like Python, Java, and R offer specialized libraries and features to handle tasks such as data analysis, decision-making, and machine learning (ML). These languages are key to building AI systems like chatbots, recommendation engines, and self-driving cars.
Whether you’re an experienced developer or just starting your coding journey, it is critical that you choose the right language for your AI projects.
In this article, we explore the top six programming languages for AI, delving into their applications and the reasons behind their popularity.
Key Takeaways
- AI programming languages are tools that help developers create software that mimics human intelligence.
- Popular AI coding languages like Python, Java, and R offer specialized libraries and features to handle various tasks such as data analysis, decision-making, and machine learning.
- The best AI programming language for you depends on your specific goals – whether you’re building AI tools, analyzing data, or exploring machine learning.
- Python is usually a favorite among developers for its simplicity and versatility, which makes it suitable for a wide range of AI projects.
- Choosing the right programming language is essential for successful AI software development.
Top 6 AI Programming Languages for Developers in 2025
| Language | Release Year & Author | Use Cases |
|---|---|---|
| Python | 1991: Guido van Rossum | Widely used for writing programs that process data, train models to recognize patterns, make predictions, and automate decision-making. |
| R | 1993: Ross Ihaka & Robert Gentleman | Best suited for tasks like data analysis, making predictions, and creating visual graphs to explain results. |
| Java | 1995: James Gosling at Sun Microsystems | Ideal for managing large datasets and building tools, such as machine learning algorithms, natural language processing (NLP) systems, and data-intensive applications. |
| Julia | 2012: Jeff Bezanson, Stefan Karpinski, Viral B. Shah & Alan Edelman | Excellent for creating machine learning models, processing vast amounts of data, and running simulations due to its efficiency with complex mathematics. |
| Scala | 2003: Martin Odersky | Primarily used for handling massive datasets and building scalable machine learning applications. |
| C++ | 1985: Bjarne Stroustrup | Known for its speed, it’s perfect for training deep learning models, building ML algorithms, and developing AI applications requiring real-time performance. |
Now that you know which programming languages are used most often in AI development, let’s take a closer look at each one in detail. Below, we explore what they are used for and why they stand out.
1. Python
Python is one of the most widely used programming languages for AI, and it is known for its popularity due to its simplicity and flexibility. It offers a large ecosystem of libraries and tools that can be used for various tasks like machine learning, natural language processing, and computer vision. Python programming is commonly used in AI to develop programs that process data, train models to recognize patterns, make predictions, and automate decision-making.
Key specialized libraries include TensorFlow, PyTorch, and Scikit-learn.
Top AI Packages & Libraries
- TensorFlow: Builds and trains machine learning and deep learning models.
- PyTorch: Helps create and train computer programs that can learn and make decisions, such as recognizing images or understanding text.
- Scikit-learn: Provides tools for machine learning tasks, such as classification and regression.
- NumPy: Essential for numerical computations and array operations.
- Pandas: Simplifies data manipulation and analysis with tables (dataframes).
- Matplotlib: Creates static and interactive data visualizations.
Komninos Chatzipapas, founder at HeraHaven.AI, told Techopedia: “Python is considered the gold standard for AI and machine learning.”
“The PyTorch and TensorFlow libraries (by Meta and Google, respectively) have become incredibly popular and provide a unified and convenient interface for interacting not just with CUDA (Nvidia) GPUs, but also with ROCm (AMD), CPU devices, and Apple Metal devices.”
However, Ria Gupta, a software engineer intern at NASA, said that because Python is an interpreted, high-level language, it can be slow for high-performance tasks, especially compared to C++.
2. R
R is a programming language widely used in AI for data analysis and model creation. It is particularly popular for tasks like analyzing data, making predictions, and generating visualizations to explain results. R has an extensive collection of tools and packages that help build AI models, process data, and identify patterns.
It is especially favored by researchers and data scientists for its ability to handle complex numerical data and make sense of complex data.
Top AI Packages & Libraries
- Tidyverse: A group of R packages that make it easy to clean, organize, and explore data, which is often necessary before building AI models. While not specifically an AI library, it’s widely used to prepare and organize data to ensure it’s ready for the AI model to analyze.
- IML (Interpretable Machine Learning): A package that helps explain how machine learning models work and makes AI results clearer and easier to understand.
- Caret: A flexible tool for training machine learning models, preparing data, and adjusting model settings.
- RandomForest: A version of the Random Forest method used for sorting data into categories (classification) or predicting numbers (regression). Known for being accurate and reliable.
- XGBoost: A fast and efficient tool for gradient boosting designed to build strong predictive models.
- Keras: Keras in R is an interface to the TensorFlow library, making it easier to build and train deep learning models directly within the R environment.
“R is your go-to for stats and visualization. For machine learning – sure, it works, but Python often does better, especially in production,” Lars Nyman, chief marketing officer of Cudo Compute, explained to Techopedia.
“Still, academia loves R, though for one reason or another.”
3. Java
Java is one of the best programming languages for AI, valued for its speed, scalability, and cross-platform compatibility. This flexible programming language is well-suited for processing substantial amounts of data. It is commonly used to build various tools like machine learning algorithms and natural language processing systems.
With its numerous libraries, such as Weka and Deeplearning4j, Java simplifies AI development.
Top AI Packages & Libraries
- Weka: A user-friendly tool that helps developers explore and experiment with data using traditional machine-learning methods.
- Deeplearning4j (DL4j): A Java-based tool for building and using advanced deep learning models.
- Deep Java library: Simplifies building, training, and deploying deep learning models.
- Smile (statistical machine intelligence and learning engine): Provides a variety of algorithms for machine learning, data analysis, and natural language processing.
- H2O.ai: Supports machine learning and AI, making it easy to build models that can handle tasks such as grouping, predicting values, and more.
- Java-ML (Java machine learning library): Simple and lightweight library that offers a variety of machine learning algorithms and tools for developers.
“Java is used for building enterprise-level AI systems, natural language processing, and processing big data through frameworks, such as Weka and DL4J,” said Arunkumar Thirunagalingam, Senior Manager of Data & Technical Operations at McKesson Corp.
“It is used for its platform independence and ability to scale with large systems.”
4. Julia
Julia is a high-performance programming language designed to handle numbers and scientific problems, making it ideal for AI and machine learning. It combines the speed of languages like C with the ease of use of Python, so developers can write fast and easy-to-understand code.
In AI, Julia is commonly used for tasks such as building machine learning models, processing large datasets, and running simulations due to its ability to handle complex mathematical computations efficiently.
Julia is particularly popular in research and specialized fields like finance and computational sciences.
Top AI Packages & Libraries
- Flux.jl: A flexible and easy-to-use library for building neural networks, great for research and production.
- Knet.jl: A fast deep learning library with dynamic graphs, perfect for complex models and efficient processing.
- MLJ.jl: A fast framework for testing, comparing, and tuning machine learning models in one place.
- Mocha.jl: A deep learning library for building and training neural networks, with support for CPU and GPU.
- StatsBase.jl: A Julia library with basic tools for statistics, such as summaries, sampling, and probability calculations.
- Makie.jl: A Julia library that helps create interactive and high-quality visualizations, such as 2D and 3D charts and graphs.
“Julia performs similarly to C/C++ with its just-in-time compilation. And its ease of use makes it accessible to people who are used to Python,” noted NASA’s software engineer intern Ria Gupta.
“Julia also supports linear algebra operations and deep learning with libraries like Flux.jl, as well as parallel computation, which are all critical for high-performance numerical computing and machine learning.”
5. Scala
One of the most common programming languages for AI, Scala, is a flexible coding language that supports both object-oriented and functional programming. This makes it great for building complex AI systems. It is primarily used in AI to handle massive amounts of data and create machine learning applications.
Scala works well with tools like Apache Spark, which are crucial for training AI models. Its clean syntax and compatibility with Java libraries make it efficient for developing AI.
Top AI Packages & Libraries
- Apache Spark: A tool for processing massive amounts of data quickly and efficiently.
- Spark MLlib: A library in Spark for building and using machine learning models.
- Breeze: A library for doing math and scientific computing, such as working with equations, solving complex problems efficiently, and running machine learning tasks.
- Deeplearning4j: A tool for building deep learning models that works well with Scala and big data platforms.
- Scalding: A Scala library that makes it easy to process and analyze big data on platforms such as Hadoop.
- Algebird: Simplifies working with data, making big data and AI processing faster and more efficient.
“Scala integrates seamlessly with Apache Spark, making it excellent for large-scale data processing and supporting robust machine learning frameworks,” said McKesson Corp’s Thirunagalingam.
“Scala combines functional and object-oriented programming paradigms.”
Cudo Compute’s Nyman agrees, adding that “Scala is built for scale. Spark and Scala are a good match for massive datasets. Maybe think of it as Python’s high-performance cousin.”
6. C++
C++ is a fast and efficient programming language widely used in AI. It is particularly well-suited for training deep learning models, developing machine learning algorithms, and building AI applications that require real-time processing.
The language is good at handling large amounts of data and boosting performance in fields such as robotics, gaming, and language processing.
Top AI Packages & Libraries
- TensorFlow C++ API: Great for large projects and production use, offering fast and scalable performance.
- PyTorch C++ Frontend (LibTorch): Easy to use for research, with flexible tools and a Python-like feel.
- Caffe: Perfect for image recognition tasks, especially with CNNs (a type of neural network).
- MLpack: Built to handle common AI tasks quickly and efficiently, even with lots of data.
- Shark: Offers a wide variety of tools to create AI models for different tasks.
- Dlib: A flexible library that’s especially good for working with images, including facial recognition.
“C++ is the speed freak, essential for that real-time stuff like self-driving cars.”
However, Nyman from Cudo Compute added: “But good lord, the debugging can feel daunting.”
The Bottom Line
In 2025, AI programming is evolving faster than ever, with popular languages like Python, Java, and Julia leading the way. The best programming language for your project depends on your specific goals – whether you are creating AI tools, working with data, or exploring machine learning.
Choosing the right programming language is key to successful AI software development, ensuring that your project is efficient, scalable, compatible with other systems, and supports rapid development.
