Machine Learning Applications in Emergency Resource Allocation in Europe: A Systematic Review and Future Research Agenda

This study systematically reviews the application of machine learning (ML) in emergency resource allocation across Europe, with the aim of synthesizing current evidence and identifying future research directions. A systematic literature review (SLR) was conducted following PRISMA guidelines. Data were collected from major academic databases (2018–2025) using predefined inclusion and exclusion criteria. A total of 52 relevant studies were analyzed through qualitative thematic synthesis. The review finds that ML significantly enhances predictive analytics, enabling accurate forecasting of emergency demand and proactive resource allocation. ML-driven optimization improves ambulance dispatch, hospital resource management, and logistics efficiency, while real-time decision support systems strengthen situational awareness and coordination. However, challenges persist, including data quality issues, system fragmentation, ethical concerns (bias, transparency), and limited interoperability across European systems. ML has transformative potential in shifting emergency resource allocation from reactive to data-driven, predictive systems. Its effectiveness, however, depends on robust data infrastructure, ethical governance, and system integration. The study recommends strengthening data systems, adopting hybrid ML-optimization models, enhancing ethical frameworks, investing in human capacity, and promoting cross-border collaboration.