Research on Energy Efficient Workflow Scheduling in Cloud Computing focuses on developing strategies to execute complex workflows across distributed cloud resources while minimizing energy consumption without compromising performance, Quality of Service (QoS), or Service Level Agreement (SLA) requirements. This area addresses challenges posed by dynamic workloads, heterogeneous resources, and large-scale cloud infrastructures. Key research directions include designing energy-aware scheduling algorithms using heuristics, metaheuristics (e.g., genetic algorithms, particle swarm optimization), and hybrid optimization techniques, as well as deadline- and priority-aware energy-efficient scheduling. Other emerging topics involve multi-objective optimization balancing energy, execution time, and cost, cloud–edge integrated energy-aware workflow scheduling for latency-sensitive applications, and adaptive real-time scheduling frameworks. Additionally, research on predictive machine learning-based scheduling, fault-tolerant energy-efficient workflow management, and green cloud computing approaches represents promising avenues for advancing sustainable, intelligent, and efficient cloud workflow execution.