Vehicular Ad Hoc Networks are a prominent method employed for enhancing road safety, predicting traffic, and improving the efficiency of road management. In this technique, vehicles get communication from roadside infrastructure as well as with each other. Due to such communications, drivers get information about real-time traffic conditions, road conditions, and safety hazards. One critical aspect of VANETs is traffic prediction and modeling, which play a crucial role in optimizing traffic flow, routing decisions, and resource allocation. This chapter provides a study of various modeling techniques in VANETs like static statistical methods, machine learning techniques, deep learning techniques, and hybrid models. The proposed chapter evaluates the strengths and limitations of these methods in predicting various traffic parameters, such as traffic volume, congestion levels, and travel times.