The integration of edge-enabled IoT into vehicular networks contributes to the fruitful development of ITS in smart cities, improving safety, responsiveness, and efficiency. Edge computing works closely to support real-time decision-making processes that are crucial for the application scenarios mentioned above, such as autonomous driving, traffic optimization, and accident prevention. This method uses the automatic decision-making abilities of machine learning and augmented deep learning methods like convolutional neural networks (CNN) for workload offloading and scheduling. Vehicle-to-everything (V2X) communication through edge nodes and smart sensors can be enhanced further to achieve an incident-free and smooth interaction between vehicles, infrastructure, and pedestrians for optimizing traffic. It addresses security, data privacy, and scalability issues, and helps build resilient and adaptive vehicular networks. Case studies from worldwide smart city initiatives show how these technologies influence the real world. In terms of latency reduction, the successive authentication method shows a 45% reduction compared to DTTCH and DRL method. When comparing the energy consumption rates, the GA method stands out with 24% relative to methods like ARO, MDP, and ECDSA. This chapter attempts to demonstrate how edge-enabled IoT in-vehicle networks may alter urban transportation and build smarter, more sustainable cities by offering a framework for deploying them.