Real-time monitoring, analysis, and control of traffic systems is made efficient by the integration of IoT and Convolutional Neural Networks (CNNs) for traffic management, which completely transforms urban mobility. Large volumes of data are gathered about traffic flow, vehicle kinds, and ambient conditions by cameras and sensors. Wireless networks are used to transfer this data to cloud, where deep learning models in particular, CNNs analyse it to find incidents, detect vehicles, and estimate traffic density. The knowledge acquired helps to optimise traffic flow, provide dynamic traffic signal regulation, and improve safety by promptly responding to incidents. The real-time capabilities of this integrated approach significantly improve the overall effectiveness of traffic management systems. The accuracy, precision, latency and scalability of the proposed system was found promising.