Research on Hybrid Workflow Scheduling in Cloud Computing focuses on combining multiple scheduling strategies—such as heuristic, metaheuristic, and machine learning approaches—to efficiently allocate and execute complex workflows across distributed cloud resources. This approach aims to optimize multiple objectives simultaneously, including execution time, cost, energy consumption, and Quality of Service (QoS), while handling dynamic and heterogeneous cloud environments. Key research directions include designing hybrid algorithms that integrate genetic algorithms, particle swarm optimization, ant colony optimization, or reinforcement learning for improved convergence and solution quality, and priority-aware or dependency-aware workflow scheduling techniques. Other emerging topics involve multi-objective hybrid scheduling for balancing makespan, cost, and energy efficiency, cloud–edge integrated hybrid scheduling for latency-sensitive applications, and adaptive frameworks for real-time workflow management. Additionally, research on fault-tolerant and SLA-compliant hybrid scheduling, predictive task allocation using machine learning, and optimization under uncertain or fluctuating workloads represents significant avenues for advancing intelligent and efficient cloud workflow management.