Cloud computing has revolutionized extensive parallel processing and distributed computation, offering computer resources through a usage-based payment model that significantly enhances accessibility and scalability. However, the effectiveness and speed of cloud services heavily depend on how tasks are scheduled and executed. Current task scheduling methods often struggle to balance performance metrics such as throughput, makespan, efficiency, and speedup, leading to suboptimal utilization of cloud resources. Addressing this critical gap, our study introduces a novel task-scheduling algorithm specifically designed for cloud computing environments. Rooted in the sine cosine algorithm, our approach is tailored to meet the unique demands of cloud setups, optimizing resource allocation and task execution. Rigorous testing across three distinct scenarios demonstrates that our algorithm outperforms existing methods in terms of throughput, makespan, efficiency, and speedup. These results highlight the practical effectiveness and efficiency of our algorithm, offering a significant advancement in optimizing task scheduling within cloud computing systems. Our work thus contributes to enhancing the performance and reliability of cloud services, supporting better resource management and user satisfaction.