Abstract <p>Cloud computing provides on-demand services with high performance and scalability over the Internet. The primary goals of task scheduling in cloud environments are to efficiently utilize available resources, minimize execution time, and ensure timely task completion. Load balancing is crucial to ensure that virtual machines (VMs) are evenly utilized. However, the key challenge in cloud computing lies in effectively balancing the load and scheduling tasks. In the proposed model, an optimal task-scheduling mechanism is designed using the State-Action-Reward-State-Action (SARSA) algorithm. The scheduling process is enhanced by the Osprey Optimization Algorithm (OOA) to select the best resources, minimizing execution time, cost, and resource utilization. Once the tasks are schelued in queue, the load balancing is carried out using the Walrus Optimization Algorithm (WaOA), which optimally balances the load across VMs based on task lifetime and response time. The performance of the proposed model is evaluated under various task conditions, and the results are compared to existing models for validation. The proposed approach demonstrates superior performance with a makespan time of 64.81 s, turnaround time of 7.05 s, waiting time of 203.48 s, response time of 70 s, scheduling time of 226 s, a success rate of 95.7%, 6.01% resource utilization, and 5.69 Mbps throughput. This method effectively minimizes resource consumption while ensuring optimal task scheduling and load balancing.</p>