Cloud computing is a technology that allows users to use and access computing resources (such as servers, storage, databases, networking, and software) over the Internet as a fee-for-service basis. It was the first to be developed to its full potential since its launch. Load balancing is one of the main factors in the mastering of cloud resources. It is the method of distributing the work to the different computing resources (like virtual machines or servers) to guarantee the best use of the resources, high system performance, and user satisfaction. Cloud load balancing is not an easy task, for there are the factors of heterogeneity of resources, dynamism of workloads, and various workload capacity of resources, that will most probably make it difficult to achieve load balancing. The essay lays the groundwork for a newly built thermal hybrid optimization algorithm known as the “ant earthworm optimization algorithm (AEOA)” which is the opportunity for these problems. AEOA is a combination of two already existing optimizers which can be found among the earthworm optimization algorithm (EOA) and the ant lion optimization algorithm (ALO), and therefore, the load balancing can be strengthened. EOA is a new technology that has been tailored to the needs of ALO so it can look for space more efficiently and accurately using the local search. The algorithm does not converge to the local-minima wearing out because it approaches the optimal solution from all possible angles. As well as that, VMs can invoke ALO (cloudlets) with the aim to relieve the burden from the EOA (elastic cloud). The algorithm I am implementing is developed in such a way that it will cut the waiting time for the jobs to be complete and thus boost the number of VMs that are handled and ensure that the tasks of high, medium, and low priorities are balanced. Essay concludes with an experimentation to show whether the algorithm is applied properly or not. The paper is about this experiment that is about the result that is the proposed AEOA load balancing and scheduling algorithm, and then, the result is compared with the present load balancing and scheduling algorithms in this work. The tests will be run on a virtualization tool designed for use here called “CloudSim”. The contributor gives an analysis of AEOA as a brand new load balancing scheme used in cloud computing architecture. It uses the EOA and ALO optimization strategies like this to have the proper resource utilization and the system performance. The paper will try to establish the effectiveness of this approach by using simulation and making comparisons with the approaches which already exist. The load balancing is a very important matter in cloud computing, because it directly affects the user satisfaction and the system efficiency.