Recent research in Deadline-aware Workflow Scheduling in Cloud Computing emphasizes optimizing task execution to meet strict deadlines while balancing cost, energy, and reliability constraints. Advanced scheduling algorithms, including probabilistic, heuristic, and meta-heuristic models, are being developed to efficiently map interdependent workflow tasks onto heterogeneous cloud resources under uncertainty. Studies integrate artificial intelligence and reinforcement learning techniques to dynamically predict task completion times and adapt to fluctuating workloads. Current works also address energy efficiency, fault tolerance, and end-to-end delay control, ensuring timely workflow completion without compromising service quality or resource utilization in large-scale cloud environments.