Recent research in Particle Swarm Optimization (PSO)-based workflow scheduling in cloud computing focuses on improving the efficiency of task execution and resource utilization by leveraging swarm intelligence principles. PSO algorithms are widely used to optimize workflow scheduling parameters such as makespan, cost, energy consumption, and load balancing in heterogeneous and dynamic cloud environments. Enhanced variants of PSO integrate adaptive inertia weights, dynamic velocity adjustments, and hybridization with algorithms like Grey Wolf Optimizer or Ant Colony Optimization to improve convergence speed and avoid local optima. These advanced PSO-based models effectively handle the multi-objective nature of workflow scheduling, ensuring improved Quality of Service (QoS) while maintaining scalability and robustness across diverse cloud infrastructures.