This review article investigates the transformation of enterprise software delivery through the integration of DevOps best practices and artificial intelligence within distributed SAP cloud environments. As organizations shift away from monolithic, single-cloud architectures toward heterogeneous landscapes incorporating the SAP Business Technology Platform, AWS, Azure, and Google Cloud the complexity of maintaining synchronization between application code and autonomous AI models has intensified. The research evaluates the transition from traditional Application Lifecycle Management to a dual-track CI/CD framework that harmonizes software updates with machine learning operations. Key methodologies such as Infrastructure as Code for multi-cloud governance, AIOps for predictive observability, and DevSecOps for maintaining digital sovereignty are analyzed. The study highlights the role of SAP AI Core in orchestrating model lifecycles and the importance of data grounding through vector engines to ensure responsible AI outputs. By addressing the critical challenges of model drift, configuration inconsistency across regions, and the global skills scarcity, the paper outlines a strategic roadmap for deploying resilient, self-healing, and AI-infused SAP applications. The review concludes that a unified, automated DevOps posture is the primary prerequisite for enterprises to move beyond reactive firefighting toward a state of continuous, intelligent innovation.