Advancing Public Health Surveillance With Artificial Intelligence: A Systematic Review of Real‐Time Data Analytics and Disease Prediction

Background The integration of artificial intelligence (AI) into public health surveillance is revolutionizing how health threats are monitored, predicted, and managed. Traditional surveillance systems often face challenges such as reporting delays, limited scalability, and inefficiencies in real‐time response. Leveraging approaches such as machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enables the analysis of extensive and diverse datasets, facilitating the generation of timely and actionable insights for disease prevention and control. Aim This review aimed to systematically explore how AI is utilized to enhance public health surveillance through real‐time data analytics and disease prediction. Methods An extensive literature search was performed using five major databases: PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar to identify relevant studies published between 2010 and 2025. Eligible studies applied AI methodologies to real‐time surveillance, utilized structured or unstructured health‐related data, and reported predictive or prescriptive outcomes. All the studies included were evaluated for methodological rigor, and the results were thematically synthesized. Results Thirty‐nine studies met the inclusion criteria. The majority employed ML and DL models such as Random Forests (RFs), while others incorporated NLP for analyzing text‐based data. AI systems were utilized for descriptive monitoring, predictive modeling of disease outbreaks, and prescriptive analytics to support resource allocation. Real‐time analytics demonstrated high accuracy and timeliness in forecasting disease trends, particularly for conditions such as COVID‐19, influenza, and dengue. Hybrid models that combined multiple AI techniques further enhanced predictive performance. Conclusion AI–driven surveillance systems hold considerable promise for transforming public health monitoring. They enable faster detection, improved forecasting, and more efficient public health responses. However, challenges remain, including data standardization, ethical governance, and infrastructure disparities. Addressing these barriers is essential for equitable, scalable AI implementation in global health surveillance.