Workflow scheduling in cloud computing is a challenging NP-Hard problem due to complex task precedence constraints and the dynamic nature of cloud resources. Metaheuristic (MH) algorithms effectively tackle NP-Hard problems, like workflow scheduling, offering near-optimal task assignments while maintaining precedence constraints. This study introduces an approach to addressing the workflow scheduling problem by employing the Firefly Algorithm (FFA) to minimize the total completion time of all tasks. The performance of the proposed FFA is systematically evaluated against the Grey Wolf Optimizer (GWO), Sine-Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Differential Evolution (DE) algorithm, with a primary focus on minimizing the makespan. The experimental results demonstrate that the proposed FFA exhibits superior convergence speed and achieves a lower mean makespan than the competing algorithms. The proposed metaheuristic-based approach offers a scalable and adaptable solution for workflow optimization in cloud environments, making it particularly relevant for contemporary industrial and scientific applications requiring high computational efficiency. The proposed method is validated through convergence analysis and statistical measures, including the mean and standard deviation of the makespan.