Research on Dynamic Task Scheduling in Cloud Computing focuses on developing adaptive strategies to assign and manage tasks across distributed cloud resources in real time, considering fluctuating workloads, heterogeneous resources, and Quality of Service (QoS) requirements. This area aims to optimize execution time, cost, energy consumption, and resource utilization while maintaining Service Level Agreement (SLA) compliance. Key research directions include predictive and workload-aware scheduling algorithms using machine learning, heuristic and metaheuristic approaches (e.g., genetic algorithms, particle swarm optimization), and priority- and deadline-aware dynamic scheduling strategies. Other emerging topics involve multi-objective optimization balancing makespan, cost, and energy efficiency, cloud–edge integrated scheduling for latency-sensitive applications, adaptive fault-tolerant scheduling, and hybrid approaches combining different optimization techniques for improved performance. Additionally, research on real-time, self-adaptive, and intelligent task scheduling frameworks represents significant avenues for advancing efficient, scalable, and autonomous cloud computing operations.