The reliability of cloud services is now increasingly determined by how safely organizations can modify their software, rather than just by the redundancy of their infrastructure. Although continuous integration and continuous delivery pipelines have shortened the time between releases, problems such as static rules, fragmented telemetry, manual approval hurdles, and a lack of feedback between deployment and operations can cause failures to become worse as cloud systems become more distributed. This study looks at the way in which artificial intelligence can be incorporated into DevOps and into automated CI/CD pipelines in order to enhance reliable cloud service delivery in Saudi Arabia. A structured integrative review of peer-reviewed research published between 2020 and 2025 was carried out in the fields of software engineering, AI-assisted testing, AIOps, observability, cloud automation, and literature on Saudi cloud adoption. Thirty studies, verified by their DOIs, were analysed using five analytical dimensions: pipeline stage, AI function, reliability mechanism, governance requirement, and relevance to Saudi enterprise adoption. The evidence shows that AI provides the most significant improvement in reliability when it functions as a bounded decision-making layer based on high-quality telemetry, rather than as an unbounded substitute for engineering judgment. Together, predictive detection of build failures, intelligent regression testing, deployment-risk scoring, anomaly detection, root-cause localisation, and feedback-driven rollback can collectively reduce the risk resulting from frequent releases. Nevertheless, the benefits in terms of reliability depend on the use of traceable data, policy controls, progressive delivery, explainable decisions, and human authority for important changes. The review puts forward a closed-loop reference model that connects code changes, automated verification, risk assessment, controlled release, runtime observability, and learning. For Saudi enterprises, the model stresses the importance of trust, service-level assurance, governance, organizational readiness, and the adoption of locally appropriate cloud operating practices. The paper ends with specific implementation priorities and a research programme aimed at validating the approach on a production scale.