<![CDATA[The increasing burden of both communicable and non-communicable diseases (NCDs) presents significant challenges for public health worldwide. The application of artificial intelligence (AI) in epidemiology has emerged as a promising tool for predicting, monitoring, and controlling the spread of these diseases. This study aims to explore the role of AI in enhancing epidemiological practices and improving public health outcomes. The research employs a systematic review methodology, analyzing 60 peer-reviewed articles on the integration of AI technologies in disease prediction and control. The findings indicate that AI, particularly machine learning (ML) algorithms, has demonstrated remarkable success in predicting disease outbreaks, identifying high-risk populations, and optimizing resource allocation. AI-driven tools have been effectively utilized in both communicable diseases, such as influenza and COVID-19, and NCDs, including diabetes and cardiovascular diseases. The study concludes that AI holds substantial potential for transforming epidemiological practices, offering more accurate forecasts and efficient interventions. However, challenges such as data privacy concerns and resource limitations in low-income settings need to be addressed. The research highlights the need for continued investment in AI technologies to strengthen global disease prevention and control efforts.]]>
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