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The blistering development of cloud-managed supply chains has augmented the enterprise of cost-conscious service orchestration, resource distribution, and information-based operational choices. Traditional pricing systems and fixed budgeting systems are also unable to manage dynamic workload, multi-tenancy consumption behaviour, and variable market demand leading to unpredictable spending and poor use of cloud resources. In this paper, I am going to present an Intelligent Cost Governance Framework that will combine predictive analytics, multi-criteria optimization, and autonomous policy enforcement to manage operational spending across the end-to-end processes of cloud supply chain management. The framework proposed uses machine learning-based demand prediction, cost anomaly detection and elasticity-sensitive redistribution of workload to smartly balance performance and spending. In addition, a rule-based governance engine is included to ensure constant compliance checks, vendor contract assessment, and mitigating services-level risks. Efforts to experimentally assess real time cloud data show a maximal reduction in costs by 35-52%, a greater visibility of costs, and monetary dangers of decentralized supply chain activities. The findings confirm that smart cost management highly enhances the sustainability of the economy and the resilience of operations in the contemporary cloud-based supply chain.