Unbalanced load poses a significant challenge for cloud-based systems, directly impacting their reliability, makespan, cost, resource utilization, and other critical performance metrics. A key advantage of cloud computing lies in its capacity for efficient workload management, a feat achievable through either heuristic or meta-heuristic optimization techniques. While numerous methods exist for scheduling and managing cloud workloads, the integration of both heuristic and meta-heuristic approaches has received considerably less attention in prior research. To bolster system performance, a hybrid strategy can effectively select optimal resources from those available. This paper therefore introduces a Cloud Load Balancing methodology that combines heuristic and metaheuristic algorithms with the aim of improving resource utilization and concurrently reducing operational costs and makespan.