Research on Genetic Algorithm (GA)-based Task Scheduling in Cloud Computing focuses on applying evolutionary optimization techniques to efficiently assign and schedule tasks across distributed cloud resources while meeting performance, cost, and Quality of Service (QoS) requirements. This approach aims to minimize makespan, execution cost, energy consumption, and resource contention in dynamic cloud environments. Key research directions include designing customized GA variants for heterogeneous and large-scale cloud infrastructures, hybrid GA methods combined with other metaheuristics (e.g., PSO, ant colony optimization) to enhance convergence and solution quality, and priority-aware GA scheduling strategies for critical or time-sensitive tasks. Other emerging topics involve multi-objective GA for simultaneously optimizing execution time, cost, and energy efficiency, adaptive GA for real-time task scheduling under dynamic workloads, and cloud–edge integrated GA scheduling. Additionally, research on fault-tolerant GA models, SLA-aware scheduling, and integration with machine learning for predictive or intelligent decision-making represents significant avenues for advancing efficient and autonomous cloud task management.