The Internet of Vehicles (IoV) is revolutionizing smart transportation by enabling real-time communication between vehicles, infrastructure, and other connected systems. While IoV enhances road safety, traffic efficiency, and user experience, it also introduces substantial security and privacy risks. Cyber threats, including unauthorized access, data breaches, and manipulation of systems, present serious risks to the integrity of IoV networks. Ensuring secure communication, protecting sensitive data, and mitigating vulnerabilities in Vehicle-to-Everything (V2X) interactions are critical for the widespread adoption of IoV technology. Machine Learning (ML) and Deep Learning (DL) are emerging as powerful tools to bolster IoV security. For instance, ML-based Intrusion Detection Systems (IDS) can spot unusual patterns in network traffic, while DL-powered authentication mechanisms enhance identity verification. Additionally, privacy-preserving techniques such as Federated Learning (FL) and Differential Privacy (DP) enable secure data processing without exposing sensitive information. Beyond AI-based security mechanisms, blockchain technology is emerging as a robust framework for secure data sharing, ensuring transparency and integrity in IoV communications. Edge and Fog computing also play a vital role by reducing latency and enabling localized data processing, which enhances security. This paper explores the integration of ML, DL, blockchain, and distributed computing in IoV security, addressing emerging challenges and potential solutions. Future research should focus on developing adaptive security frameworks that can evolve alongside the ever-changing IoV landscape, ensuring the development of a resilient and privacy-conscious transportation ecosystem.