Abstract: Cloud registration facilitates the exchange of information and provides consumers with assets, charging them only for the resources they use. Cloud computing stores data and maintains information accessibility. However, in open circumstances, information hoarding escalates rapidly. Stack adjustment serves as a test during cloudy weather, while load adjustment distributes dynamic workloads across hubs to prevent overloading, thereby optimizing resource usage and enhancing system performance. A majority of available calculations enable stack adjustment and improved asset utilization in cloud computing, utilizing memory, CPU, and system stacks. Load adjustment detects overloaded hubs and redistributes the load to underloaded ones, ensuring equitable resource allocation across the shared system’s cloud data centers. This study proposes a hybrid load balancing method, combining Honey Bee (HB) with Particle Swarm Optimization (PSO), aiming to achieve an acceptable response time. The hybrid algorithm is tested using the CloudSim simulator, demonstrating faster reaction times compared to Honey Bee (HB) and Particle Swarm Optimization (PSO) load balancing techniques. The research evaluates response time, request servicing, data center loading, and cost in virtual machines using the simulator.