Recent research in meta-heuristic-based workflow scheduling in cloud computing focuses on enhancing the efficiency, cost-effectiveness, and energy performance of workflow execution through intelligent optimization algorithms. Approaches using Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and newer bio-inspired techniques like Harris Hawks Optimization and Honey-Bee Algorithms have shown significant improvements in minimizing makespan, execution cost, and resource imbalance. These meta-heuristic frameworks efficiently handle the complex, multi-objective nature of workflow scheduling by exploring and exploiting the search space for optimal task-resource mapping. Hybrid and adaptive meta-heuristic models further improve scalability and robustness, making them highly suitable for dynamic and heterogeneous cloud environments.