The rapid urban expansion and increasing vehicle count in modern cities have intensified the demand for efficient and flexible parking management strategies. This study explores the integration of AI and IOT technologies to improve VANETs for smart parking solutions in urban environments. It investigates how AI algorithms, combined with IOT-linked sensors and communication systems, can enhance parking operations, boost vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, and alleviate traffic congestion. The proposed framework employs machine learning techniques for predictive analytics and real-time data management to dynamically allocate parking spaces based on availability and demand patterns. It also evaluates how deep reinforcement learning influences the improvement of VANET routing protocols to increase data transmission efficiency and reduce latency. Additionally, this study highlights the significance of edge computing and cloud-based systems in enabling seamless data sharing and effective decision-making. The study emphasizes real-world applications and case studies that demonstrate the efficacy of AI-based VANET models by addressing significant issues such as data security, network scalability, and system interoperability. It also examines the potential of integrating blockchain technology to ensure data integrity and protect transactions in smart parking systems. The findings presented in this study aim to provide urban planners, policymakers, and technology developers with actionable insights for creating sustainable and smart parking solutions, thus promoting the overarching objective of smart cities and progress in urban mobility.