###### Abstract  
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person's future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health. artificial intelligence; edge computing; early health prediction; federated learning; wearable devices; chronic diseases; data privacy; public health; healthcare informatics; data analysis +
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By combining the strengths of AI and edge computing, healthcare providers can create innovative solutions that improve public health outcomes, lower healthcare costs, and improve the overall patient experience [9; 10]. Early health prediction plays a critical role in improving public health by enabling healthcare providers to identify and address potential health problems before they become more serious [11; 12]. Using a variety of data sources, including electronic health records, demographic data, and wearable devices, the field of early health prediction manifests as a multifaceted approach that provides a holistic perspective on a person's well-being and proactively anticipates impending health prognoses. This information can then be used to develop personalized interventions, treatments, and preventive measures to improve an individual's health and prevent the onset of chronic diseases [13]. One of the most important benefits of early health prediction is the early detection of chronic diseases such as diabetes, cardiovascular disease, and cancer [14; 15; 16]. By detecting these diseases in their early stages, physicians can take therapeutic measures and recommend lifestyle changes that can improve an individual's health while preventing the disease's relentless progression. It is worth emphasizing that early health prediction promises to reduce healthcare costs by circumventing the need for later exhaustive and financially burdensome treatments. Early health prediction has great potential for infectious disease prevention and control. Careful examination of data obtained from wearable devices and various sources makes it possible to identify variations in a person's activity patterns and heart rate that serve as potential precursors to the onset of an infectious disease. With this knowledge, proactive measures can be taken to limit the transmission of the disease and strategic interventions can be made to mitigate its effects. This article explores the role of Edge AI in early health prediction and highlights its benefits, limitations, and real-world applications in healthcare. It discusses the current state of the field and the challenges to be overcome. It also explores the future potential of Edge AI for improving public health.