Abstract

Traffic congestion is a critical urban challenge, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic management systems often lack real-time adaptability, resulting in inefficiencies. This paper explores the transformative role of artificial intelligence (AI) in optimizing traffic flow, predicting congestion, and improving signal control. By leveraging machine learning, computer vision, and IoT integration, AI-driven systems enhance decision-making, reduce delays, and improve road safety. Realworld implementations demonstrate significant improvements in traffic efficiency and sustainability. Despite challenges such as data privacy and infrastructure compatibility, AI holds immense potential in shaping the future of intelligent transportation systems. Keywords: AI Traffic Management, Machine Learning, Smart Cities, Intelligent Transportation Systems (ITS), Computer Vision, IoT, Reinforcement Learning, Traffic Congestion, Autonomous Vehicles.