Delivering shared data, software, and resources across a network to computers and other devices, the cloud computing paradigm aspires to offer computing as a service rather than a product. The management of the resource allocation process is essential given the technology's rapid development. For cloud computing, task scheduling techniques are crucial. Use scheduling algorithms to distribute virtual machines to user tasks and balance the workload on each machine's capacity and overall. This task's major goal is to offer a load-balancing algorithm that can be used by both cloud consumers and service providers. In this paper, we propose the ‘Bat Load’ algorithm, which utilizes the Bat algorithm for work scheduling and the Honey Bee algorithm for load balancing. This hybrid approach efficiently addresses the load balancing problem in cloud computing, optimizing resource allocation, make span, degree of imbalance, cost, execution time, and processing time. The effectiveness of the Bat Load algorithm is evaluated in comparison to other scheduling methods, including bee load balancer, ant colony optimization, particle swarm optimization, and ant colony and particle swarm optimization. Through comprehensive experiments and statistical analysis, the Bat Load algorithm demonstrates its superiority in terms of processing cost, total processing time, imbalance degree, and completion time. The results showcase its ability to achieve balanced load distribution and efficient resource allocation in the cloud computing environment, outperforming the existing scheduling methods, including ACO, PSO, and ACO and PSO with the honey bee load balancer. Our research contributes to addressing scheduling challenges and resource optimization in cloud computing, providing a robust solution for both cloud consumers and service providers.