Saudi Arabia’s shift towards prevention, integrated care and digital health creates an opportunity to use artificial intelligence at population scale rather than only for isolated diagnosis. This review examines how AI-enabled population health analytics can combine clinical, administrative, insurance, pharmacy, laboratory and patient-generated data to predict chronic disease risk and preventable hospital admissions. Thirty peer-reviewed studies published from 2020 to 2025 were synthesised across Saudi health-system transformation, digital maturity, chronic disease prediction, electronic records, remote monitoring, risk stratification, readmission prediction, explainable AI and avoidable hospitalisation. The evidence shows that machine-learning models can identify elevated risk for diabetes, cardiovascular disease, sepsis, emergency use and readmission, but predictive performance alone does not create prevention. Population benefit depends on reliable longitudinal data, clinically meaningful prediction horizons, transparent risk thresholds, human review and direct links to primary-care outreach, screening, medication review, case management and remote monitoring. Claims and encounter data provide breadth, while laboratory values, vital signs, clinical notes and patient-reported information improve clinical specificity. The review proposes a six-layer Population Health Intelligence Framework comprising multi-source data integration, longitudinal patient resolution, AI risk modelling, clinical validation, preventive care orchestration and outcome-based model learning. Preventable admissions should be treated as a system outcome requiring both accurate risk detection and timely access to effective primary care. For Saudi Arabia, the framework aligns predictive analytics with Vision 2030 goals for prevention, efficiency and patient-centred care while emphasising interoperability, privacy, fairness, explainability and accountable clinical governance.