Recent studies in Priority-based Workflow Scheduling in Cloud Computing focus on enhancing task execution efficiency by assigning dynamic priorities to workflow tasks based on factors such as task dependencies, execution time, and resource requirements. Researchers are integrating reinforcement learning, fuzzy logic, and meta-heuristic algorithms to develop intelligent schedulers that allocate high-priority tasks to optimal virtual machines while maintaining fairness and minimizing makespan. These approaches aim to improve resource utilization, reduce task waiting time, and ensure the timely completion of critical workflows in heterogeneous cloud environments, thereby achieving better Quality of Service (QoS) and overall system performance.