Research on Particle Swarm Optimization (PSO)-based Task Scheduling in Cloud Computing focuses on leveraging the population-based optimization capabilities of PSO to efficiently allocate and schedule tasks across distributed cloud resources. The primary goals are to minimize execution time, reduce cost, optimize resource utilization, and ensure compliance with Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Key research directions include designing PSO variants tailored for dynamic, heterogeneous, and large-scale cloud environments, hybrid PSO approaches combined with other metaheuristics (e.g., genetic algorithms or ant colony optimization) to improve convergence and solution quality, and priority-aware PSO strategies for critical or time-sensitive tasks. Other emerging topics involve multi-objective PSO for balancing makespan, cost, and energy efficiency, adaptive PSO for real-time task scheduling under fluctuating workloads, and cloud–edge integrated PSO for latency-sensitive applications. Additionally, research on fault-tolerant PSO scheduling, SLA-aware optimization, and integration with machine learning for predictive or intelligent decision-making represents promising avenues for advancing autonomous and efficient cloud task management.