Research on Priority-based Workflow Scheduling in Cloud Computing focuses on developing strategies to schedule tasks within workflows according to their priority levels, ensuring that critical or time-sensitive tasks are executed efficiently while optimizing overall system performance. This area aims to minimize makespan, execution cost, and resource contention while meeting Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Key research directions include designing priority-aware scheduling algorithms that consider task dependencies, resource availability, and dynamic workload fluctuations, as well as integrating heuristic and metaheuristic methods (e.g., genetic algorithms, particle swarm optimization) for improved optimization. Other emerging topics involve multi-objective priority-based scheduling balancing cost, energy efficiency, and execution time, cloud–edge integrated scheduling for latency-sensitive tasks, and adaptive frameworks for real-time workflow management. Additionally, research on fault-tolerant and SLA-compliant priority-based scheduling, predictive task prioritization using machine learning, and hybrid scheduling approaches combining priority with optimization techniques represents significant avenues for advancing efficient and intelligent cloud workflow management.