The rapid expansion of cloud services and the increasing reliance on them have made load balancing a significant research challenge. As technological services grow in demand, optimizing their performance becomes essential. This study addresses the load-balancing challenge by mathematically formulating the problem within cloud computing systems, where an objective function is developed to minimize response time, computational cost, and load imbalance, while ensuring constraints such as task allocation to individual virtual machines (VMs) and adherence to resource limits. To solve this problem, we propose a novel hybrid algorithm, ACOCSA, which combines ant colony optimization (ACO) and crow search algorithm (CSA). Our experimental results indicate substantial performance improvements. Specifically, ACOCSA reduces response time by 12% compared to ACO, achieving a reduction from 8.5 to 7.48 s …