Comparative Analysis of Python’s Role in AI and Machine Learning

Abstract

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has led to the widespread adoption of various programming languages. Among them, Python has emerged as the dominant choice due to its extensive libraries, ease of use, and strong community support. However, alternative languages such as R, C++, Java, and Julia also play significant roles in AI/ML development. This paper presents a comparative analysis of Python against these languages, evaluating factors such as performance, scalability, library support, ease of implementation, and industry adoption. The study highlights Python’s advantages, including its rich ecosystem and developer-friendly syntax, while also addressing its limitations, such as execution speed and memory consumption. Through empirical analysis and case studies, this research provides insights into the suitability of different languages for AI/ML applications, offering recommendations for selecting the optimal language based on specific project requirements.