Lessons from Global Health Crises: The Role of Machine Learning and AI in Advancing Public Health Preparedness and Management

Abstract

With recent global health crises, the amalgamation of Machine Learning (ML) and Artificial Intelligence (AI) has developed into a revolutionary solution for the upliftment of public health management and preparedness. The research looks into how four well-known ML algorithms—Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost—have been utilized for forecasting and health emergency management. A comprehensive data set of multiple public health indicators was used to train and test each model. Experimental results showed that the XGBoost algorithm performed better than others with an accuracy rate of 96.3%, followed by Random Forest with 94.8%, SVM with 91.5%, and Decision Tree with 88.2%. These results prove the high performance of ensemble learning models for complicated real-world health prediction tasks. Further, the research synthesizes existing literature in healthcare, environmental science, and emergency response fields, outlining effective AI-based approaches and enumerating shared issues like data privacy, algorithmic bias, and ethical deployment. The study concludes that AI and ML models have the potential to greatly enhance early-warning systems, enhance outbreak control, and aid real-time decision-making, if they are integrated into ethically robust and interdisciplinary frameworks