{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T04:12:51Z","timestamp":1769227971361,"version":"3.49.0"},"reference-count":196,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator\u2019s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver\u2019s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML.<\/jats:p>","DOI":"10.3390\/fi17020079","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T11:08:51Z","timestamp":1739358531000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9132-1963","authenticated-orcid":false,"given":"Navdeep","family":"Bohra","sequence":"first","affiliation":[{"name":"Department of CSE\/IT, Maharaja Surajmal Institute of Technology, New Delhi 110058, India"}]},{"given":"Ashish","family":"Kumari","sequence":"additional","affiliation":[{"name":"Department of CSE\/IT, Maharaja Surajmal Institute of Technology, New Delhi 110058, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5481-1368","authenticated-orcid":false,"given":"Vikash Kumar","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Cape Town, Rondebosch 7700, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0632-5044","authenticated-orcid":false,"given":"Pramod Kumar","family":"Soni","sequence":"additional","affiliation":[{"name":"Department of Computer Applications, Manipal University Jaipur, Jaipur 302007, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5032-8966","authenticated-orcid":false,"given":"Vipin","family":"Balyan","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics, and Computer Engineering, Cape Peninsula University of Technology, Cape Town 8000, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6782","DOI":"10.1109\/JSEN.2016.2583382","article-title":"A Survey on Heuristic-Based Routing Methods in a Vehicular Ad-Hoc Network: Technical Challenges and Future Trends","volume":"16","author":"Hajlaoui","year":"2016","journal-title":"IEEE Sens. 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