Cloud computing has emerged as a dominant paradigm for delivering scalable and on-demand computing services to a wide range of applications, including enterprise systems, data analytics, and Internet of Things (IoT) platforms. Despite its widespread adoption, ensuring consistent Quality of Service (QoS) while efficiently utilizing cloud resources remains a critical challenge, particularly in multi-tenant and dynamic environments. Fluctuating workloads, heterogeneous resource requirements, and strict Service Level Agreement (SLA) constraints often lead to performance degradation, inefficient resource usage, and increased operational costs. To address these challenges, this paper presents an intelligent QoS-aware resource optimization framework aimed at enhancing performance efficiency and SLA compliance in cloud computing environments. The proposed framework integrates workload profiling, QoS analysis, adaptive scheduling, and multi-objective optimization to dynamically allocate resources based on real-time system conditions. A comprehensive optimization model is formulated to simultaneously minimize response time, execution cost, and SLA violations while maximizing resource utilization. Unlike conventional static or heuristic-based approaches, the proposed solution continuously adapts to workload variations using a feedback-driven decision mechanism. The effectiveness of the framework is evaluated through simulation-based experiments under diverse workload scenarios. Performance metrics such as response time, throughput, resource utilization, and SLA violation rate are analyzed and compared with traditional scheduling techniques. The results demonstrate that the proposed approach achieves significant improvements in performance efficiency and QoS assurance, making it suitable for modern cloud infrastructures supporting latency-sensitive and resource-intensive applications. The outcomes confirm that intelligent, QoS-driven optimization is essential for sustainable and high-performance cloud service provisioning.