This research presents a novel approach to dynamic scheduling in cloud computing environments, directing on budget-constrained workflows and towards optimizing makespan, Quality of Service (QoS) metrics, and energy efficiency. Exploiting the Task Duplication Scheduling Algorithm (TDSA) and Salp Swarm Algorithm (SSA) which is influenced by the motion patterns of marine life forms, the proposed Enhanced Salp Swarm Algorithm (ESSA) algorithm dynamically assigns tasks to cloud resources while bearing budget curbs. The algorithm seeks to reduce makespan, guaranteeing efficient completion of workflows, at the same time boosting the QoS metric resource utilization. Moreover, the incorporation of energy-efficient scheduling techniques further donates to the sustainability of cloud environment operations. By constructing a mathematical representation which captures the trade-off between makespan, resource utilization, and budget constraints, the mechanism productively balances competing objectives to reach optimal scheduling outcomes. Through substantial experimentation with 5 Scientific Workflows, the success and efficiency of the suggested methodology are assessed, exhibiting its potential to significantly improve the performance of budget-restricted workflow in cloud environments while boosting workflow’s makespan (up to 9%) and improving asset usage (up to 5%) and energy efficiency (up to 10.5%). This research presents to advancing the latest in dynamic scheduling techniques for cloud environment, benefitting practical solutions for real-world deployment and operation.