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Optimizing resource utilization and task execution within scalable cloud computing infrastructures remains a paramount challenge for service providers. This paper proposes and empirically evaluates a novel framework for intelligent resource orchestration, leveraging advanced learning algorithms to dynamically enhance performance. Our methodology integrates reinforcement learning principles to adaptively manage heterogeneous cloud resources, aiming to minimize task completion times and maximize system throughput. Through rigorous simulation experiments, this study demonstrates a significant improvement in resource allocation efficiency compared to conventional scheduling paradigms. The findings offer a strategic blueprint for developing autonomous and cost-effective cloud management systems, paving the way for next-generation adaptive cloud services.