In response to the rapid urbanization of cities, Intelligent Traffic Management Systems are needed to reduce traffic congestion. As part of this paper, we present the design and implementation of an Intelligent Traffic Management System (ITMS) that improves traffic flow and optimizes urban mobility utilizing machine learning (ML) and Internet of Things (IoT) technologies. By monitoring, analyzing, and predicting traffic congestion points using IoT sensor data in real-time, the proposed system monitors traffic conditions, analyzes patterns, and optimizes traffic flow. A traffic signal control system and route optimization software work together to adapt to changing traffic conditions using advanced machine learning algorithms. Simulated and real-world deployments of the system proved its effectiveness, reducing travel times and reducing emissions and traffic flow significantly. In this study, the researchers highlight the potential of integrating machine learning and the IoT in creating smart cities that improve transportation efficiency and sustainability.