<p>Load balancing is the allocation of operations or workloads across several resources to improve efficiency, performance, or dependability. It prevents overloading of any one resource and thus enhances scalability and fault tolerance. This paper discusses a novel load-balancing approach to advance multi-cloud environments. We propose a novel Intelligent Earth-Worm Optimization (IEWO) algorithm to optimize load balancing with task scheduling. A time-based load balance (TBLB) framework is designed to calculate the fitness function using makespan, which will enable the identification of solutions having the best load-balancing performance among those with the same makespan. The method advantages the capability to identify the solution with the greatest load balancing performance among a group of solutions with identical makespan. More crucially, the interplay between makespan and TBLB improves the algorithm by simultaneously minimizing makespan. We assess IEWO through multiple task scheduling difficulties while comparing it to other metaheuristic algorithm-based load-balancing tasks. The findings demonstrate that IEWO could accomplish extremely competitive outcomes while preserving resilient load balancing qualities, outperforming other existing approaches in both makespan and TBLB desired outcomes.</p>