Urbanization and the rapid increase of vehicles in metropolitan areas have intensified traffic congestion, increased travel time, and augmented road accidents. Traditional traffic management systems, typically reliant on static signal timing and manual interventions, lack adaptability and real-time responsiveness. The integration of Internet of Things (IoT) technologies and real-time analytics presents opportunities to develop intelligent traffic systems capable of optimizing flow dynamics, reducing wait times, and enhancing road safety. This paper presents the design and performance evaluation of an IoT-enabled Smart Traffic Management System (STMS) that uses real-time data from sensors and communication networks. The system architecture combines edge computing, cloud services, and machine learning analytics to enable dynamic signal control, congestion prediction, and priority routing for emergency vehicles. Using a combination of simulated and real-world traffic data, the STMS is evaluated on parameters such as throughput, latency, signal efficiency, and average travel time. Results demonstrate significant improvements in traffic coordination, up-to-30% reduction in average delays, and enhanced prediction accuracy. Future directions include integration with autonomous vehicles, expanded machine learning models, and deployment in multi-city environments. Urbanization and the surge in vehicle numbers have placed unprecedented strain on metropolitan traffic systems, resulting in chronic congestion, prolonged travel durations, and a rise in road accidents. Conventional traffic management approaches, which primarily depend on fixed signal timings and manual control, are increasingly inadequate due to their inability to respond dynamically to fluctuating traffic conditions. The advent of Internet of Things (IoT) technologies combined with real-time data analytics offers a transformative solution by enabling the development of adaptive and intelligent traffic management frameworks. These systems leverage continuous data streams from sensors and communication networks to monitor traffic patterns, predict congestion, and adjust signal timings proactively, thereby optimizing traffic flow and enhancing overall road safety.

The proposed IoT-enabled Smart Traffic Management System (STMS) integrates edge computing, cloud infrastructure, and machine learning algorithms to facilitate real-time decision-making and dynamic control of traffic signals. This architecture supports advanced functionalities such as congestion forecasting and priority routing for emergency vehicles, which are critical for reducing delays and improving response times. The system's performance, validated through simulations and real-world traffic datasets, demonstrates substantial gains in throughput and signal efficiency, achieving up to a 30% reduction in average travel delays. These improvements highlight the potential of the STMS to significantly enhance urban mobility. Future research directions include expanding the system’s capabilities through integration with autonomous vehicle networks, refining machine learning models for greater predictive accuracy, and scaling deployment across multiple urban centers to address diverse traffic environments.