Recent research in Energy-Efficient Workflow Scheduling in Cloud Computing focuses on optimizing energy consumption while maintaining performance and meeting workflow deadlines in large-scale cloud environments. Modern approaches integrate advanced optimization and learning algorithms such as Deep Reinforcement Learning, Particle Swarm Optimization, and hybrid meta-heuristics to efficiently allocate tasks across virtual machines with minimal power usage. These studies emphasize dynamic task clustering, workload prediction, and energy-aware VM selection to reduce idle energy and enhance sustainability. Energy-efficient workflow scheduling models also consider Quality of Service (QoS) constraints, renewable energy integration, and carbon footprint reduction. Overall, the latest works aim to balance computation efficiency, cost reduction, and environmental sustainability in executing complex workflows within cloud infrastructures.