This paper discusses the main problems and difficulties in implementing Intelligent Transportation Systems (ITS). These include the need to minimize delays in communication between automobiles and Roadside Units (RSUs), assuring a continuous flow of traffic, and improving road safety. The relevance of Vehicular Ad hoc Network (VANET) has attracted attention from several academic domains. Continuous monitoring is necessary for these systems to use Machine Learning algorithms on large amounts of data from VANET applications such as crowdsourcing, pollution control, and environmental surveillance. Deep Learning enables computers to enhance their performance by analyzing historical data. The objective of VANET is accomplished by the effective use of supervised and unsupervised data learning methods. The hybrid deep learning model is created by merging the Convolutional Neural Network (CNN) with the Bidirectional Long Short-Term Memory model that incorporates Elman gates (BLSTME). We conducted an analysis of the dependability, communication, and difficulties linked to traffic in VANET networks, and found that they are not feasible to execute in practice. In addition, we explored the possibilities of using machine learning techniques to tackle these difficulties. In the end, we discussed the future direction and challenges, and analyzed a case study that demonstrated a situation related to VANET.