This study uses machine learning models to investigate cognitive computation’s potential applications in real-time traffic control. Congestion in metropolitan areas is a rising problem, hence new methods are needed to better manage traffic lights. Incorporating state-of-the-art machine learning strategies like Reinforcement Learning and Deep Q-Networks, the suggested approach stands out as a promising option. The suggested system learns from past data and real-time input to adjust to ever-changing traffic circumstances, making static signal timing plans obsolete. Our research of performance shows that the suggested technique regularly outperforms state-of-the-art alternatives like Fixed Signal Timing Control and Actuated Signal Control. This study compares the outcomes of two groups that use both unique and tried-and-true strategies to improve traffic light timing accuracy. The assessment, which will be based on simulated data, will be primarily concerned with the overall efficacy of the techniques. Deep Reinforcement Learning and Fuzzy Logic, two new techniques, scored better on average, with Deep Reinforcement Learning scoring 80.73 and Fuzzy Logic scoring 65.21. When employing traditional approaches such as adaptive control and fixed timing, two averages of 49.42 and 48.36 are comparable. We need to collect a large amount of data and models since our research covers over a hundred urban crossings with varying traffic intensity patterns. The first findings suggest that more current technologies, such as reinforcement learning, may enhance traffic flow by 30% when compared to more traditional ways. When the adjustment was in place, the average wait time at intersections during peak hours fell from 2.5 min to 1.75 min. This shows that the improvement was successful in reducing wait times. This study uses a wide range of visualization approaches to illustrate its findings, which helps to emphasize the benefits and drawbacks of each methodology. The ultimate objective is to find traffic management systems that successfully blend innovative thinking with tried-and-true procedures. The excellence of the suggested system is shown by its average performance ratings, which show that it can ease traffic congestion, speed up travel times, and increase productivity. This study shows how cognitive computing and machine learning models may improve traffic management in real time, leading to reduced congestion and lower energy costs for urban transportation networks.