The massive growth of IoT-based data has increased the necessity to schedule the tasks effectively and distribute the resources in the cloud environment efficiently. Conventional cloud load-balancing approaches are usually incapable of handling diverse workloads, ensuring high resource underutilization, and reducing delays in dynamic operating environments. To overcome such obstacles, the paper proposes a Hybrid Momentum-Pyramid Optimization Algorithm (HMPOA) based IoT-Cloud Scheduling Model that combines the features of Momentum Search Algorithm (MSA) and Giza Pyramid Construction Algorithm (GPCA). The main objective is to efficiently schedule and allocate IoT tasks in a cloud environment. The proposed method maximizes trust, delay and distance metrics to determine optimal execution paths and assign tasks evenly across the virtual machines. The performance of the proposed method is evaluated in terms of higher resource utilization, throughput, energy efficiency, and makespan compared to recent methods. The numerical results show that, at 30 VMs, the proposed method has 84.77% utilization, 5-12% more than APOA, Meta-RHDC, and HDWOA-LBM. Similarly, at 60VMs, the proposed method achieves high scalability 86.90%, compared to APOA of 5.43%, and HDWOA-LBM of 11.8%.