Recent research in Hybrid Workflow Scheduling in Cloud Computing explores combining heuristic, meta-heuristic, and machine learning approaches to optimize workflow execution across heterogeneous cloud environments. Hybrid models leverage the strengths of traditional algorithms like HEFT with advanced optimization techniques such as Whale Optimization, Genetic Algorithms, and Deep Reinforcement Learning to achieve a balance between makespan, cost, energy consumption, and resource utilization. These integrated strategies adapt dynamically to varying workloads and system conditions, enabling efficient scheduling of complex workflows while meeting QoS constraints. The latest studies highlight that hybrid methods outperform single-approach algorithms in scalability, convergence speed, and overall workflow performance in large-scale cloud systems.