Abstract

The increasing frequency and intensity of epidemic outbreaks underscore the urgent need for intelligent, real-time health surveillance systems. This review explores the development of a smart health risk monitoring framework that leverages artificial intelligence (AI) to predict epidemic trends and support strategic resource planning. By integrating machine learning algorithms, spatiotemporal data analytics, and health informatics, the framework enables early detection of outbreak signals, dynamic trend forecasting, and proactive resource allocation. The study highlights the role of AI in processing vast and heterogeneous datasets from electronic health records, social media feeds, IoT-enabled sensors, and public health databases to model disease transmission patterns and identify emerging hotspots. Furthermore, the review examines how predictive insights generated by the framework can guide public health decisions, optimize healthcare resource distribution, and enhance emergency response mechanisms. Key challenges, including data privacy, model explainability, and infrastructure limitations, are also discussed, alongside proposed solutions. The paper aims to provide a comprehensive roadmap for deploying AI-driven epidemic surveillance systems that ensure operational readiness and resilience in health systems worldwide.