Abstract
The prediction of epidemic outbreaks is a critical aspect of public health management, as early detection can mitigate the spread of diseases and save lives. Artificial intelligence (AI) and neural networks have shown great promise in analyzing large-scale health data to predict epidemic outbreaks. This article explores the application of AI, particularly deep learning and neural networks, in predicting the occurrence of epidemics, forecasting disease spread, and identifying potential risk factors. We discuss the challenges, data requirements, and future potential of AI-driven models in epidemic prediction, emphasizing the role of machine learning in strengthening global health responses
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