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Information is the driving force in vehicular ad hoc networks (VANET) since vehicles share information (emergency, general, and multimedia). VANET communicates between vehicles using a unique routing protocol, unlike other wireless routing technologies. Many protocols, techniques, and approaches have been developed to secure and protect data. To enhance current security and privacy measures and develop and model new ones, the ideas of machine learning (ML), deep learning (DL), and artificial intelligence are being applied. In this paper, we provide information on the various types of attacks that target VANET communication, VANET layers, the security goals that are affected, and real-time attacks that occur on manufacturing hubs. We compared various VANET attack prevention, detection, and AI techniques proposed, as well as future research work in the field of VANET, for improving accuracy, security, and privacy. [ABSTRACT FROM AUTHOR]