Cloud operations have become a strategic reliability function as enterprises move customer journeys, public services, industrial platforms and data products to hybrid and multi cloud environments. Traditional monitoring practices are no longer sufficient as cloud systems generate high-velocity telemetry from containers, serverless functions, networks, databases, identity services and user-facing applications. This review looks at how artificial intelligence for IT operations (AIOps) can improve predictive monitoring, controlled automation and service reliability. The paper uses a structured narrative review approach to synthesise literature, standards and implementation guidance published from 2020 to 2025. The review identifies five operational domains, namely observability engineering, incident prediction, root cause analysis, automated remediation, and governance for trustworthy operations. It argues that it is not possible to improve service reliability just through machine learning models, but also needs good telemetry, topology awareness, safe runbooks, cyber resilience, human validation and continuous feedback from incidents. Saudi Arabia is particularly relevant for AI-enabled cloud operations as Vision 2030 digital transformation, cloud-first adoption, smart city initiatives, financial technology, healthcare platforms and critical digital infrastructure require highly available and resilient services. The study proposes a layered architecture linking logs, metrics, traces and service dependency graphs with predictive analytics, automation policy and reliability dashboards. It also gives a maturity roadmap for organisations that want to transition from reactive monitoring to proactive, self-improving operations. The review concludes that AIOps can reduce mean time to detect and recover, improve capacity planning and support business continuity, provided that organisations treat automation as a governed socio-technical capability rather than a purely technical tool.