<p>Workflow scheduling in cloud computing plays a vital role in optimizing resource utilization, minimizing execution time, and maintaining Quality of Service (QoS) under dynamic and heterogeneous conditions. However, existing metaheuristic and heuristic scheduling techniques often struggle with premature convergence, inefficient resource allocation, and poor adaptability in large-scale scientific workflows. To address these challenges, this study proposes CASSTO (Cloud-Aware Seal Search Task Optimization), a bio-inspired metaheuristic algorithm that models adaptive search behavior through combined Lévy and Brownian walks to achieve balanced exploration and exploitation. CASSTO integrates dynamic task prioritization, adaptive cost modeling, and QoS-aware scheduling mechanisms within the WorkflowSim simulation framework. Experimental evaluations on benchmark scientific workflows (Montage and SIPHT) demonstrate that CASSTO achieves notable improvements in makespan reduction, cost optimization, and resource utilization when compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. The results validate CASSTO as an effective and scalable approach for QoS-driven workflow scheduling in cloud computing environments.</p>