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Efficient hospital resource allocation is a persistent operational challenge because demand for beds, staff, equipment, diagnostics, and patient movement changes rapidly. Predictive analytics offers a way to anticipate demand and support more proactive operational decisions. This systematic review synthesised machine learning models applied to hospital resource allocation from 2017 to 2023. The review focused on bed management, staffing demand, equipment use, diagnostic capacity, and patient throughput. A PRISMA 2020-compliant review process was used, including structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science. Screening, extraction, risk-of-bias assessment, and narrative synthesis were conducted to compare model targets, data sources, validation approaches, and implementation maturity. The evidence base was dominated by retrospective studies of patient throughput, emergency department admission prediction, bed demand, and length of stay. Staffing, equipment, and diagnostic capacity forecasting were less frequently represented, while integrated multi-resource command centre models remained uncommon. Machine learning for hospital resource allocation has become technically advanced, but operational translation remains uneven. Most models remained retrospective or locally validated, with limited evidence of prospective deployment, workflow integration, or measurable operational impact.

Foundation models pre-trained on massive, multimodal data have transformed several areas of artificial intelligence. Their adaptation to healthcare operations analytics is an emerging frontier because hospital workflows generate dense streams of structured, textual, temporal, and administrative data. This systematic review examined applications of foundation models to patient flow prediction, documentation burden estimation, resource allocation, and operational risk forecasting from 2017 to 2026. The review focused on multimodal pretraining, transfer learning, downstream adaptation, validation, and implementation maturity. A PRISMA 2020-compliant search was designed for PubMed, Scopus, IEEE Xplore, and Web of Science. Records were screened by two reviewers, and eligible studies were synthesized narratively by operational domain, model architecture, data modality, and maturity level. A small but rapidly growing body of work suggests that pre-trained multimodal models may support operational prediction tasks across healthcare systems. Evidence was concentrated in patient flow and resource allocation, while documentation burden estimation and operational risk forecasting were less frequently studied. Foundation models show potential to unify operational analytics across hospital systems. The field remains immature, with limited external validation, few prospective implementation studies, and no widely adopted benchmarks for operational foundation models.