This paper discusses the completion of a complete ALPR model and a new vehicle density-based approach to traffic signal management. The system estimates the density of passing cars to dynamically adapt wait times for signals optimally to improve traffic flow. The state-of-the-art object detection model is used for license plate detection and segmentation, which is YOLOv9. For character recognition, the Inception and DenseNet deep learning architectures are used with modified GELU and Swish activation functions to enhance accuracy. It shall use a targeted dataset of Gujarat licence plates to ensure proper identification at the local level. The performance of the system in estimating vehicle density and ALPR is excellent. Hence, the system is suitable for traffic management and other associated applications in law enforcement. The results indicate a possibility of unprecedented improvement in traffic flow, among other perspectives, inefficiency in law enforcement among many others, adaptable to controlling various conditions in traffic, thus making this innovative urban environment solution very promising.