Vehicular ad hoc networks (VANETs) overhauled the way the modern transportation system works changed the way. This chapter is designed to target the key elements and several issues faced by the systems using VANETs. This chapter explores the key components and challenges of VANETs, which includes but not limited to security concerns, protocols for communication, use of deep learning and machine learning for analyzing traffic flow, etc. This chapter also addresses the deployment requirements of the VANET architectures with special attention being given to the energy efficiency, resource utilization and randomness of the VANET environment. Since vehicles swiftly change their locations, which results in interactions with different topologies and network dynamics. Since regular Machine Learning algorithms, which often are based on a pre-defined deterministic model doesn’t adapt well with dynamic VANET environments, deep learning algorithms are used along with the reliable and secure mechanisms to protect the VANET system against malicious attacks. All these requirements demand a comprehensive framework customized specially for VANETs. This chapter proposes a novel framework for VANETs that addresses intricacies of the VANET environment while leveraging the potentials of communication technology and deep learning to address the underlying challenges.