Years ago, a worrying number of road accidents circulated throughout the internet globally. These accidents resulted in the loss of 3.6 thousand lives and the injury of 217 thousand people. When we consider that many people are on the road throughout the whole day, the importance of intelligent adaptive road management systems becomes clear. Traffic management systems are being improved by incorporating communication opportunities to enhance these features. Vehicular Ad Hoc Networks (VANETs) are used to constitute communication opportunities. VANETs are a wireless ad hoc network with a dynamically changing topology that consists of vehicles. Vehicles can communicate with other vehicles or roadside units. VANETs are a crucial component of intelligent transportation systems. VANETs have several applications such as safety, traffic management, infotainment, and comfort. To meet these applications, VANETs offer distinctive characteristics: in VANETs, the connection between the vehicle and the roadside is wireless and movable, and the traffic environment is highly dynamic. There are many prominent works to improve advanced intelligent transportation systems via VANETs. Apart from these works, artificial intelligence techniques such as machine learning and deep learning have not been widely studied by researchers to improve VANET applications. In this study, we intend to investigate machine learning and deep learning models to enhance the capabilities of the applications that are offered in VANETs. By offering vehicular ad hoc networks a hybrid deep learning and machine learning model, we aim to enrich VANET applications with artificial intelligence aspects and further work on these topics. With this study, we plan to fill the research gap in the exploration of VANET models with respect to machine learning and deep learning models.