The Internet of Things is made up of vehicular ad-hoc networks (VANETs), which are used for routing path applications to monitor computational domains. Nonetheless, there are still research gaps that need to be filled, and traffic-related issues might be handled with the use of optimization techniques that take the Internet of Vehicles into account. This chapter proposed opposition-based learning strategy with honey badger method (OBLHBA), a heterogeneous algorithm presented in this research, combines two cutting-edge algorithms for effective routing that enhances energy efficiency, boosts throughput, and reduces end-to-end latency. Although proposed OBLHBA method exhibit high performance compared to existing method such as multi-objective delay centric enhanced artificial ecosystem-based optimization (MDCEAEO), ant colony optimization and artificial bee colony optimization (ACO-ABCO), and particle swarm optimization (PSO). The proposed OBLHBA method yields high results, achieving a rounding overhead 0.088 rounds, packet delivery ratio (PDR) of 99.45%, and an end-to-end delay (ETED) of 0.003 s, respectively.