• T H Kumar
• R Sumathi

Intelligent Transportation Systems (ITS) integrate advanced sensing, communication, and computational technologies to improve traffic safety, mobility efficiency, and environmental sustainability. With the rapid growth of urban populations and vehicle ownership, traditional traffic management approaches are no longer sufficient to address congestion, accidents, and emissions. Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) have significantly transformed ITS by enabling real-time traffic prediction, adaptive control, accident analysis, and autonomous mobility. This article presents an extensive literature review of ITS with a strong focus on ML and AI techniques. The review covers ITS architecture, data sources, communication technologies, major application domains, learning models, big data analytics, challenges, and future research directions. The paper aims to provide a comprehensive reference for researchers and practitioners working on intelligent, data-driven transportation systems.

Intelligent Transportation Systems, Machine Learning, Artificial Intelligence, Traffic Prediction, Smart Mobility, Big Data Analytics

"Intelligent Transportation Systems: A Comprehensive Review of Machine Learning and Artificial Intelligence Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.f469-f472, January-2026, Available :http://www.jetir.org/papers/JETIR2601569.pdf

"Intelligent Transportation Systems: A Comprehensive Review of Machine Learning and Artificial Intelligence Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppf469-f472, January-2026, Available at : http://www.jetir.org/papers/JETIR2601569.pdf