With the growth in the number of vehicular applications, there has been significant need for more processing and communication capabilities in the heterogeneous systems. Vehicular Ad-Hoc Networks or simply VANETs have been established with the aim of improving traffic congestion, accidental rates, and safety for drivers through vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure V2I communication. However, as a result of constant changes in the vehicular environment due to vehicle dynamics to include mobility, limited broadcast range and the adaptive network formation, there are sets back in scalability and performance. Another challenge that affects the adoption of autonomous driving systems and the Internet of Vehicles (IoV) is security threats, privacy issue, and data management challenges. This paper reviews the use of fog computing and artificial intelligence (AI) techniques to solve these challenges. Fog computing provides the efficient and distributed processing capacity and ML facilitates intelligent decision-making in real-time applications such as traffic flow analysis, obstacle identification, and prediction of the safety measures required in a vehicle. Thus, the paper discusses how such technologies can help in increasing traffic safety in IoV, coordinating the real-time data analysis, and implementing efficient resource utilization. In addition, the future innovations like blockchain mobility and artificial intelligence are included and their use in mitigating the problems in existing vehicular communication networks noted. In this paper, the author’s objective is to review the advancements in technology in relation to smart transport systems and the possibility of their development in the future.