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The evolution toward 6G\u2010enabled vehicular networks and the integration of heterogeneous sensors in modern vehicles have created new opportunities for improving routing efficiency, mobility management, scalability, and security in vehicular IoT (VIoT). However, highly dynamic topologies and strict quality\u2010of\u2010service requirements introduce significant technical challenges. Machine learning (ML), particularly neural network\u2013based approaches, has emerged as a promising solution for addressing these issues. Despite the rapid growth of ML\u2010driven VIoT research, a structured and up\u2010to\u2010date systematic review remains limited. This paper presents a comprehensive systematic review of ML applications in VIoT, analyzing 30 selected studies and categorizing them using a newly proposed taxonomy based on training mechanisms and data utilization strategies. The reviewed works are comparatively evaluated in terms of application domains, performance metrics, challenges, and future research directions. The findings demonstrate that ML techniques enhance routing performance, enable accurate traffic congestion prediction, and support intelligent decision\u2010making. Nevertheless, security, scalability, and real\u2010world deployment remain open challenges requiring further investigation.<\/jats:p>","DOI":"10.1155\/int\/5375075","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T08:28:56Z","timestamp":1776760136000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Applications of Machine Learning Algorithms in Vehicular Internet of Things: A Structured Literature Review and Avenue for Future Research Agenda"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9837-0857","authenticated-orcid":false,"given":"Bing","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihong","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"e_1_2_14_1_2","article-title":"Blockchain-Based Verifiable Decentralized Identity for Intelligent Flexible Manufacturing","author":"Xu W.","year":"2025","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_14_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/mnet.2019.1800236"},{"key":"e_1_2_14_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2021.3107310"},{"key":"e_1_2_14_4_2","doi-asserted-by":"crossref","DOI":"10.1109\/TITS.2023.3271452","article-title":"Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks","author":"Yao Y.","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_2_14_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2918255"},{"key":"e_1_2_14_6_2","doi-asserted-by":"crossref","DOI":"10.1109\/TITS.2025.3606554","article-title":"Rm 2 Occ: Re-Projection Multi-Task Multi-Sensor Fusion for Autonomous Driving 3d Object Detection and Occupancy Perception","author":"Ren Y.","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_2_14_7_2","article-title":"Left Gaze Bias Between LHT and RHT: A Recommendation Strategy to Mitigate Human Errors in Left-and Right-Hand Driving","author":"Xu J.","year":"2023","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"e_1_2_14_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2023.3298110"},{"key":"e_1_2_14_9_2","article-title":"A Review of Vision-Based Traffic Semantic Understanding in ITSs","author":"Chen J.","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_2_14_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2018.2884525"},{"key":"e_1_2_14_11_2","article-title":"Collaborative Perception and Computing Offloading in 6G Air-Ground Integrated Networks","author":"Jiang H.","year":"2025","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"e_1_2_14_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3169488"},{"key":"e_1_2_14_13_2","doi-asserted-by":"publisher","DOI":"10.7307\/ptt.v36i6.667"},{"key":"e_1_2_14_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2018.2828651"},{"key":"e_1_2_14_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e21639"},{"key":"e_1_2_14_16_2","article-title":"Quantifying the Contribution of Environmental Variables to Cyclists\u2019 Exposure to PM2. 5 Using Machine Learning Techniques","volume":"10","author":"N\u00fa\u00f1ez M. 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