In smart city environments, the rapidly urbanized city and transitioning transportation system structure require innovative ways to improve efficiency, safety, and autonomy. With real-time communication between vehicles, infrastructure, and pedestrians, Intelligent Vehicular Ad-Hoc Networks (VANETs) have become transformative technology. Unfortunately, urban environments are dynamic and unpredictable by nature, making it very hard for traditional VANET systems to work properly. Integrating ML algorithms into VANETs can address these challenges through improved decision-making, predictive analytics, and autonomous functioning. Exchange Data using Fusion and Machine learning with Multi agent vehicles in Intelligent VANET (Autonomous). VANETs use ML algorithms from data as sensors, traffic, camera and connected devices in the real time. You will know from above reference that Machine learning in VANETs is not only predicting traffic patterns or optimizing routing but even accident prevention. Moreover, ML-based intrusion detection systems further add a layer of security by helping to identify and neutralize any potential threats, thus maintaining secure VANET operations. Machine learning (ML) integrated vehicular ad hoc networks (VANETs) can unlock enhanced user experiences and the potential for safer driving. Different ML case studies also highlight its use in transportation systems for traffic management, autonomous driving systems, and emergency response systems, offering enhancements in safety, efficiency, and sustainability. Finally, the paper covers some challenges such as data privacy, computational complexity, interoperability, etc., and suggests future research for bolstering the integration of ML with VANETs. This integration sets the stage for increasingly intelligent, secure, and autonomous urban transport systems by leveraging the strengths of these types of networks.