The ever-increasing pace of urbanization and the increase in population have heightened the demand for innovative traffic management strategies aimed at ensuring sustainable mobility and alleviating congestion in smart cities. Conventional traffic management systems, which depend on fixed infrastructure and limited data integration, find it challenging to adapt to the ever-changing urban landscape. Hence, we need advanced technologies such as Vehicular Ad Hoc Networks (VANETs), which utilize vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to facilitate real-time data exchange. The synergy of AI and VANETs represents a significant advancement. Advancement toward the development of intelligent traffic systems which can perform predictive analytics, make adaptive decisions, and optimize traffic flow seamlessly. Machine learning algorithms are used in AI-enhanced VANETs. This makes possible to process extensive real-time traffic data, allowing for precise predictions of traffic trends, prompt identification of incidents, and effective route planning. Additionally, this integration aids in the navigation of autonomous vehicles, coordination of emergency responses, and the reduction of carbon emissions through efficient energy management. This research looks at how AI can enhance Vehicle Ad Hoc Networks (VANETs) and transform urban transportation. It explores the potential benefits, such as improved efficiency and sustainability, while also addressing challenges like cybersecurity risks, infrastructure requirements and policy considerations. By integrating AI with VANETs, cities can create smarter and more connected transportation systems, shaping the future of urban mobility.