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Public health emergencies reveal critical weaknesses in healthcare supply chains, especially when PPE demand outpaces procurement and distribution capacity, making predictive analytics an important tool for forecasting demand and improving allocation during crises. This systematic review evaluates predictive analytics models for PPE demand forecasting and distribution optimization during public health emergencies, focusing on model types, data sources, validation approaches, performance metrics, equity considerations, and implementation readiness. Following PRISMA 2020 guidelines, searches were conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2017 and 2025, yielding 2,847 records, of which 35 met inclusion criteria. Included studies comprised time series and statistical models (34%), machine learning and hybrid approaches (29%), optimization methods (26%), and simulation or digital twin frameworks (11%), with limited evidence of real-world deployment. Overall, findings indicate that predictive analytics can enhance PPE supply chain resilience by improving demand forecasting, allocation decisions, and scenario testing, but widespread adoption is limited by poor data interoperability, insufficient prospective validation, weak equity integration, and limited operational integration into healthcare decision systems.

Healthcare operations are constrained by demand volatility, resource scarcity, staffing pressures, and interdependent patient pathways. Artificial intelligence and predictive analytics offer a way to anticipate operational stress before it becomes visible in queues, bed shortages, overtime, or delayed care. This systematic review examines predictive analytics models applied to hospital staffing, scheduling, bed capacity, patient flow, and service demand forecasting from 2017 to 2022. The objective is to synthesize model types, data sources, operational targets, validation approaches, and implementation maturity across these domains. A PRISMA 2020–compliant review design was used to guide database searching, screening, eligibility assessment, extraction, and synthesis. Searches covered PubMed, Scopus, IEEE Xplore, and Web of Science, with narrative synthesis grouped by operational domain and risk of bias considered using an operationally adapted PROBAST-AI lens. The evidence base was dominated by retrospective, single-centre studies demonstrating the technical feasibility of predictive analytics for bed demand, emergency department arrivals, admission prediction, discharge prediction, and length-of-stay estimation. Staffing and scheduling studies were less frequent, and prospective implementation in real operational workflows remained uncommon. Predictive analytics for healthcare operations management is technically mature but practically under-deployed. The central challenge is translating forecasts into staffing, scheduling, bed-management, and command-centre decisions that measurably improve operational performance.