Cloud computing has emerged as a paradigm that transcends traditional distributed computing systems, such as Grid and Cluster systems, offering the capability to handle dynamic requests and diverse user requirements. As the number of users grows, there is a pressing need to deploy effective mechanisms for load balancing and task scheduling. Load balancing is essential for evenly distributing workloads across physical servers, preventing resource congestion, and enhancing overall system performance. Furthermore, considering users' service requests and the necessity for service providers to deliver accurate and timely responses, coupled with the limited resources available in the cloud environment, efficient task scheduling becomes imperative. This paper proposes an approach for optimizing virtual machine (VM) migration by combining Genetic Algorithms and Ant Colony Optimization for resource scheduling operations. Additionally, it employs K-Means clustering and fuzzy logic to quantify the dependencies between VMs and physical machines during migration, thereby maintaining load balance. The proposed model is evaluated and compared against three existing load balancing algorithms within the CloudSim simulation environment. The evaluation results demonstrate that our proposed model achieves a 4.5% reduction in task completion time, a 4.9% increase in the deadline success rate, and a 3.9% improvement in task diversity. Furthermore, computational complexity is reduced by 8.3%, VM migration efficiency is improved by 2.5%, and decision-making delay is significantly decreased by 9.5%. Additionally, the model achieves substantial energy savings of 30-35%.