<p>Energy-efficient task scheduling in cloud systems is critical for maximizing resource utilization and lowering operational expenses. Energy savings provide numerous substantial benefits, including decreased running costs, increased system efficiency, and environmental preservation, making them an important issue in cloud computing systems. However, there is a significant barrier to matching user needs with available cloud resources in order to provide optimal performance while reducing energy usage within a user-defined timeframe. Therefore, we present the energy-efficient priority-based task-scheduling algorithm (e-PTSA), which performs priority-based task scheduling while considering energy and performance. The proposed technique consists of two phases: classification and allocation. To boost overall system performance, tasks are allocated in parallel to vitrual machines (VMs). We evaluated e-PTSA's effectiveness in optimizing crucial parameters such as average resource utilization and energy consumption through careful evaluation. The outcome demonstrates the efficacy of the proposed approach in achieving energy-efficient task scheduling in heterogeneous cloud environments while maximizing resource utilization. The experimental results of e-PTSA are evaluated using cutting-edge approaches to demonstrate its effectiveness in comparison with other state-of-the-art approaches.</p>