Recent research in Scientific Workflow Scheduling in Cloud Computing focuses on optimizing performance, cost, and energy efficiency for executing large-scale scientific applications across distributed cloud infrastructures. Researchers have proposed advanced scheduling frameworks that leverage meta-heuristic algorithms such as Whale Optimization, Genetic Algorithms, and Particle Swarm Optimization, alongside emerging machine learning and reinforcement learning approaches to improve decision-making in dynamic cloud environments. Studies emphasize workflow-aware resource provisioning, VM-task pairing, and energy-efficient scheduling in geo-distributed data centers to ensure minimal makespan and balanced load distribution. The latest works also integrate sustainability and QoS objectives, highlighting hybrid optimization strategies and intelligent workflow orchestration for scalable and energy-conscious cloud-based scientific computing.