As vehicle ad hoc networks (VANETs) grow rapidly, AI is essential to the breakthrough advancements in intelligent transportation systems. Traffic control using deep learning (DL) and machine learning (ML) algorithms and self-organized decision-making in VANETs has been made possible by the rapid development of autonomous and connected cars and the rising need for road safety. An ideal traffic flow and risk awareness mode were achieved with a variety of machine learning approaches, including support vector machines, random forests, and reinforcement learning. However, DL models give smart-based collision estimates as well as enhanced techniques to assist an aircraft in avoiding collisions. Convolutional Neural Networks (CNNs) are these models. RNNs and LSTMs are two examples. However, there are still problems with privacy, real-time processing, and data diversity. Preparing networked, connected car systems with contemporary technologies like edge computing, federated learning, and adversarial robustness to make VANET systems scalable and safe is one of the next challenges. Digital twin models, collaborative AI, and fifth-generation, or 5G, connectivity are examples of new technologies that promise to advance VANETs by enhancing their environmental, security, and adaptability. This chapter highlights the prospective directions that have not yet been investigated and gives a summary of the current AI tools and issues in VANET. AI-powered VANETs can revolutionize the current transportation ecosystem and provide the most sophisticated and intelligent solution to the challenges it faces. To meet the demands of the constantly connected and urban environment, they can also offer safer transportation.