S Sunil Kumar , G Lokesh 2 4 (October-December) The Internet of Vehicles or IoV is a disruptive technology that comes with very minor latency time and high bandwidth support for the interconnection of the entire traffic vehicles roads and users But this huge interconnection also makes vehicular networks vulnerable to various attacks like denialofservice impersonation botnets and zeroday attacks The paper provides an overview of the latest trends in intrusion detection systems enabled by artificial intelligence AI such as machine learning ML Deep learning DL and hybrid methods in IoV networks Aspects like ensemblebased training deep neural design knowledge distillation and privacypreserving systems such as federated learning and homomorphic encryption are being discussed The performance evaluation conducted on both the actual and standard datasets shows that these complex MLDL architectures are not only highly accurate but also very fast with short delays and are capable of detecting both regular and new threats Other challenges like unbalanced data lowpower devices zeroday attacks and model interpretability are also examined Moreover the recent progress made in AIbased IDS and privacyaware systems points towards a trend of a scalable secure and trusted IoV The paper provides an overview of current trends emphasizes necessary future research and gives a glimpse of resilient IDS that would be able to secure vehicular networks in advanced and automated transportation systems Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026