Efficient task scheduling is a critical challenge in cloud computing due to the dynamic nature of the cloud environment and the diverse requirements of tasks. This paper proposes a hybrid meta-heuristic scheduling approach that combines the strengths of multiple optimization techniques to enhance the performance of independent task scheduling in cloud computing. The proposed method integrates Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to exploit the global search capabilities of GA and the fast convergence properties of PSO. Experimental results demonstrate that the hybrid approach significantly improves task scheduling efficiency, achieving lower makespan and higher resource utilization compared to traditional single-method techniques. This study highlights the potential of hybrid metaheuristic approaches in addressing complex scheduling problems in cloud computing environments.