Research on Scientific Workflow Scheduling in Cloud Computing focuses on efficiently managing and executing complex, data-intensive scientific workflows across distributed cloud infrastructures. This area aims to optimize multiple objectives such as execution time, cost, energy consumption, and resource utilization while ensuring compliance with Quality of Service (QoS) and Service Level Agreements (SLAs). Key research directions include heuristic- and metaheuristic-based scheduling algorithms (e.g., genetic algorithms, particle swarm optimization, ant colony optimization), deadline- and priority-aware scheduling strategies, and multi-objective optimization for scientific workflow execution. Other emerging topics involve adaptive and real-time workflow scheduling in dynamic cloud environments, cloud–edge integration for latency-sensitive scientific applications, and fault-tolerant and resource-aware scheduling frameworks. Additionally, research on predictive and machine learning-based scheduling for workflow performance estimation, energy-efficient workflow orchestration, and hybrid scheduling techniques combining heuristics, metaheuristics, and soft computing approaches represents significant avenues for advancing intelligent, scalable, and efficient scientific workflow management in cloud computing.