It is essential to find creative solutions to the growing urban problems of traffic congestion and parking issues. By using real-time traffic camera photos with image processing and deep learning algorithms to compute traffic density in real-time, the suggested approach gets around these problems. The system speeds up transit and lowers pollution in megacities by reducing traffic dramatically and dynamically modifying traffic lights based on vehicle density. The study also presents a novel solution to the issue of people circling open lots in search of parking spots. The system achieves a remarkable positive detection rate by using thermal cameras and cutting-edge deep learning architectures using Yolov4. The overall accuracy of the YOLO v4 model in this hypothetical scenario is approximately 0.90. Parking lot cruising patterns are illuminated by the precise tracking of several moving automobiles made possible by Kalman filters. In addition to improving traffic control, this combination of state-of-the-art technologies provides insightful information about parking behavior, opening the door to more effective urban mobility solutions.