Research in Genetic Algorithm (GA)-based Task Scheduling in Cloud Computing focuses on optimizing task allocation and resource utilization to achieve efficient execution in dynamic cloud environments. GA-based techniques use evolutionary operations such as selection, crossover, and mutation to explore optimal task–resource mappings that minimize makespan, cost, and energy consumption. Recent approaches integrate adaptive parameter tuning and hybrid models combining GA with other metaheuristic algorithms to enhance convergence speed and scheduling efficiency. These strategies improve load balancing, fault tolerance, and overall system performance, making GA-based scheduling a robust and scalable solution for complex cloud infrastructures.