Vehicular Ad-Hoc Networks (VANETs) facilitate vehicle-to-vehicle and vehicle-to-infrastructure communication in likely environments. Poor network congestion management, vulnerability to security threats, and inadequate Quality of Service under high-demand conditions are some limitations of traditional VANET protocols. In existing intrusion detection systems rely on static rules, making them ineffective against emerging threats, while traditional data dissemination techniques struggle to distribute critical information efficiently. This chapter focuses on limitations through deep learning-enhanced solutions, using neural networks, convolutional models, and reinforcement learning. These approaches maximize intrusion detection, increases QoS resource allocation and enhance data dissemination techniques like broadcasting and geocasting. Even in dynamic circumstances, the suggested approaches guarantee dependable and secure communication by providing predictive and adaptable capabilities. The outcome demonstrates that deep learning significantly enhances VANET performance, leading to better traffic management, improved safety, and efficient coordination of autonomous vehicles.