Research on Particle Swarm Optimization (PSO)-based Workflow Scheduling in Cloud Computing focuses on leveraging PSO, a population-based metaheuristic algorithm, to optimize the execution of complex workflows across distributed cloud resources. This approach aims to minimize execution time, cost, energy consumption, and resource contention while ensuring Quality of Service (QoS) requirements. Key research directions include designing PSO variants tailored for dynamic and heterogeneous cloud environments, hybrid PSO algorithms combined with other optimization techniques (e.g., genetic algorithms or ant colony optimization) for improved convergence, and task-priority-based PSO scheduling strategies. Other emerging topics involve multi-objective PSO for balancing execution cost, makespan, and energy efficiency, adaptive PSO for real-time workflow management, and cloud–edge integrated workflow scheduling. Additionally, research on incorporating uncertainty handling, resource failures, and SLA constraints into PSO-based scheduling, as well as leveraging machine learning to enhance PSO performance, represents significant avenues for advancing intelligent and efficient cloud workflow management.