Published Paper Details:

Artificial Intelligence (AI) has become the new technological paradigm that changes the industry of healthcare, finance, education, manufacturing, and autonomous systems. The strategic choice of programming languages is the main aspect of AI creation as it has a direct impact on the system performance, scalability, development efficiency, and deployment possibility. The paper is an analytical review of the programming languages in relation to Artificial Intelligence, the discussion of their development from symbolic AI to machine learning and deep learning models in modern times. The article assesses high-level languages such as Python, R, C++, Java, Lisp, and Prolog covering their paradigms, strengths, weaknesses, and areas of use. The research is comparative and descriptive in nature as it examines the suitability of language in fields of AI, including machine learning, deep learning, natural language processing, computer vision and robotics. The results have shown that high-level languages have high prototyping speed and large ecosystem advantages, whereas low-level languages offer better execution speed and hardware-level accessibility. The paper adds up to the conclusion that there is no single programming language that can fit all AI requirements; a hybrid and multi-language architecture is the best approach to the development of modern AI systems. The study offers advice to researchers, developers, and higher learning institutions on the suitable programming language that can be used, depending on the complexity of the project, the performance, and the scaling aspects of the project.