Research in artificial intelligence (AI) techniques for Vehicular Ad Hoc Networks (VANETs) focuses on leveraging intelligent and data-driven algorithms to enhance routing, traffic management, security, and communication efficiency in dynamic vehicular environments. Recent studies employ machine learning, deep learning, and reinforcement learning approaches for predictive routing, congestion control, and adaptive communication under varying mobility and network conditions. Swarm intelligence and fuzzy logic–based algorithms are also explored for clustering, resource allocation, and decision-making in distributed vehicular systems. AI-driven intrusion detection and trust management frameworks are being developed to strengthen security and resilience against cyberattacks. Moreover, the integration of edge computing and federated learning enables real-time, privacy-preserving AI processing directly within vehicles. These advancements collectively enhance reliability, scalability, and intelligence in next-generation VANETs, supporting applications like autonomous driving, intelligent transportation, and smart city connectivity.