Diversified number of vehicular applications with the rapid advent in technology is capable of producing high data volume which need to be executed when the vehicular nodes are dynamically moving in the network. However, the resources which are essential for computing as necessitated by the vehicles are restricted in terms of energy and processing during the process of handling these large volumes of data generated by these vehicular applications. The cloud datacenters, even though possess the capability of processing the massive data generated, are determined to very distance resulting in huge delay. Hence edge computing is essential for addressing the issues of clouds in terms of huge transmission delays since it minimizes the cost of communication since the distance is considerably reduced between the vehicle and the allocated resources. In this paper, an Improved Beluga Whale-based Task Offloading Mechanism (IBWTOM) is proposed for maximizing resource utilization in Vehicular Edge Computing Network. This IBWTOM specifically used the merits of Beluga Whale Optimization Algorithm (BWOA) which is enhanced using a dynamic update factor and Cauchy mutation operator for the objective of accelerating the convergence rate during the phase of exploitation and guarantee maximized population diversity for preventing the solutions from being trapped into the local point of optimality. It used a fitness function which integrated the factors of execution time, delay and energy into account during the process of decision-making whether the tasks of the vehicular nodes need to be executed in the cloud of the edge computing environment. This inclusion of improved BWOA helped in ensuring the required level of reliability and effectiveness during the process of exploration and exploitation such that it is well balanced during the task offloading process. The results of this IBWTOM confirmed minimized execution time, delay and energy independent to the size of the task considered for execution in the vehicular edge computing environment.