SummaryVehicular fog networks (VFNs) improve vehicular communications by incorporating fog computing into the existing vehicular infrastructure. The integration of fog computing and vehicular communication results in the development of a diverse range of novel applications and services, including intelligent transportation systems and real‐time data analytics. This paradigm has evolved as an innovative strategy that deals with the load on cloudlet nodes, improves service response time during high‐demand periods, and saves energy in battery‐powered devices. VFN is realized by offloading user workloads to vehicular devices, taking advantage of the underutilized computational power of nearby vehicles. While VFN holds great promise, its widespread adoption faces many challenges, including ineffective energy‐latency tradeoff mechanisms and optimal resource allocation strategies. This study provides a thorough analysis that explores the challenges, examines current approaches, and suggests ways to improve the tradeoffs between energy and latency, thereby optimizing network resources. The study offers constructive recommendations for enhancing the development and establishing more robust VFNs.