Urban areas across the world are facing increasing traffic congestion, environmental pollution, and rising road accidents due to rapid population growth and motorization. Traditional traffic management systems, often based on fixed timing or manual control, lack the adaptive intelligence needed to optimize flow in dynamic road environments. This research article presents a Smart Traffic Management System (STMS) leveraging Machine Learning (ML) and Image Processing (IP) methodologies to improve real-time traffic prediction, incident detection, vehicle classification, and signal optimization. The system integrates data from cameras, sensors, and historical traffic patterns to construct an intelligent control framework. Core contributions include an adaptive signal control module powered by reinforcement learning, image-based vehicle detection and classification through deep learning (CNNs), and predictive modeling for congestion forecasting. Results from simulations and real-world pilot deployments demonstrate significant reductions in average waiting times, improved throughput, and enhanced incident responsiveness when compared with conventional traffic systems. This study concludes with discussions on scalability, implementation challenges, and future research directions.

The adaptive signal control module continuously learns and adjusts signal timings based on real-time traffic conditions, enhancing traffic flow efficiency. Deep learning models employed for vehicle detection achieve high accuracy in classifying different vehicle types, enabling more precise traffic analysis. Predictive congestion models utilize both historical and real-time data to forecast traffic patterns, allowing proactive management strategies to be implemented.