<p>The widespread utilization of cloud services has increased the challenges, primarily in resource efficiency and timely task execution. The conventional methods often struggle to handle multi-objective constraints such as energy consumption, latency, and resource utilization in terms of heterogeneous loads, low response times, suboptimal load distributions, as well as Virtual Machine (VM) failures. To overcome these limitations, hybrid optimization techniques are introduced to achieve balanced, adaptive, and efficient scheduling across dynamic and heterogeneous cloud infrastructures. In order to allocate positions on all tasks and their scheduled tasks efficiently and reliably, the present research proposes the Haliaetus Rapid Position Allocation-based Load Balancing and Task Scheduling (HsRPA-LBTS) scheme. The suggested HsRPA algorithm integrates the strengths of the Osprey, Red Kite, and Bald Eagle optimizers that are hybridized and dynamically respond to workload variations with the consideration of energy constraints. A multi-objective fitness function further improves memory-aware task assignment, optimizing resource utilization, processing time, and overall system efficiency. During performance evaluation, the HsRPA-LBTS achieves a higher throughput ratio of 0.915 and an average resource utilization ratio of 0.799, with a lower degree of imbalance 0.07, and a makespan of 37.43s in the presence of 50 VMs for executing 1000 loads under a simulation environment.</p>