The increasing rate of urbanization and the increased need for efficient transportation have highlighted the shortcomings of traditional traffic management. Pre-programmed infrastructure, human observation, and static traffic signals are examples of outdated methods that usually fall short in handling real-time congestion and unpredictable traffic patterns. The constantly evolving needs of modern urban transportation are also not met by them. To solve these problems, cities are utilizing artificial intelligence (AI) and vehicular ad hoc networks (VANETs). This convergence creates adaptable, data-driven frameworks that transform traffic systems. By combining state-of-the-art technologies like deep learning for predictive analytics, reinforcement learning for dynamic signal optimization, and edge computing for real-time data processing, AI-enhanced VANETs increase the capabilities of traffic networks. These clever solutions lessen congestion via anticipating interruptions and proactive traffic rerouting. We are moving from antiquated, inflexible systems to intelligent networks driven by AI. Increasing vehicle communication, improving road safety, and optimizing traffic flow all depend on them. Using case studies and actual data, we demonstrate how AI-powered frameworks speed up route planning and reduce travel delays. They also support sustainable smart city technologies that are scalable. The results demonstrate AI’s ability to both address present inefficiencies and pave the way for upcoming advancements in urban transportation.