Recent research in hybrid metaheuristic algorithm-based task scheduling in cloud computing focuses on integrating multiple optimization techniques such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) to achieve efficient resource utilization, reduced makespan, and improved load balancing. These hybrid models leverage the exploration capability of one algorithm and the exploitation strength of another to overcome the limitations of traditional or single metaheuristic approaches. Studies highlight that combining swarm intelligence with evolutionary computation significantly enhances task mapping efficiency, energy savings, and response time under dynamic cloud environments. Overall, hybrid metaheuristic scheduling frameworks have proven to be more adaptable and effective for handling complex multi-objective optimization problems in large-scale cloud infrastructures.