To tackle the critical problem of urban traffic congestion, the Adaptive Traffic Control System (ATCS) dynamically optimizes traffic signal timings in real-time by leveraging cutting-edge machine learning algorithms. Conventional traffic signal systems frequently have fixed timing schedules that don't adjust to the way traffic flows. This results in longer wait times and more fuel being used. In order to learn and anticipate traffic patterns, the ATCS integrates data from cameras and GPS-enabled vehicles. The ATCS may modify traffic signals according to the flow, density, and speed of vehicles by continuously observing and evaluating real-time data, Traffic lights can be changed by the ATCS in response to the flow, speed, and density of vehicles. With the help of this adaptive strategy, the system can react to changing traffic volumes throughout the day, giving priority to traffic flow during peak hours and making dynamic adjustments in the event of unforeseen disruptions like accidents or road closures. By lowering emissions linked to idling cars, the ATCS deployment not only improves traffic management efficiency but also advances environmental sustainability. Comparing the ATCS to traditional fixed-timing systems, simulation tests show a considerable reduction in average waiting times and total travel duration.