This review explores the dynamic field of predictive modeling for disease outbreaks, focusing on data sources, modeling techniques, accuracy, challenges, and future directions. It highlights the importance of diverse data sources, including epidemiological, environmental, social media, and mobility data. The review discusses various modeling approaches, from statistical models to advanced machine learning algorithms and network analysis. It emphasizes the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and ethical use of personal data. The review outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies like AI and IoT to enhance predictive capabilities. The authors stress the importance of continuous innovation and collaboration to address the complex dynamics of disease outbreaks and inform public health decisions.This review explores the dynamic field of predictive modeling for disease outbreaks, focusing on data sources, modeling techniques, accuracy, challenges, and future directions. It highlights the importance of diverse data sources, including epidemiological, environmental, social media, and mobility data. The review discusses various modeling approaches, from statistical models to advanced machine learning algorithms and network analysis. It emphasizes the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and ethical use of personal data. The review outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies like AI and IoT to enhance predictive capabilities. The authors stress the importance of continuous innovation and collaboration to address the complex dynamics of disease outbreaks and inform public health decisions.