<![CDATA[Emerging infectious diseases continue to pose significant global health challenges due to increasing population mobility, environmental changes, and complex transmission patterns. Conventional epidemiological surveillance systems often experience limitations in detecting early outbreak signals because of delayed reporting and fragmented data sources. This study aims to develop a big data-based epidemiological surveillance model for improving early detection capabilities and strengthening future pandemic preparedness. The research employed an integrated computational approach combining big data analytics, artificial intelligence, machine learning algorithms, and heterogeneous data sources, including epidemiological records, mobility patterns, healthcare information, environmental indicators, and digital health signals. The results demonstrated that the proposed model achieved enhanced predictive performance by identifying early outbreak indicators before substantial increases in confirmed cases occurred. The integration of multidimensional datasets improved detection accuracy, responsiveness, and understanding of complex disease transmission dynamics. The study concludes that big data-based epidemiological surveillance provides a transformative framework for shifting pandemic management from reactive responses toward proactive prevention strategies. ]]>
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