Accurate and timely prediction of infectious disease outbreaks is critical for effective public health response. In this study, we developed a machine learning framework that integrates real-time epidemiological data, social media signals, environmental variables, and policy interventions to forecast influenza and COVID‑19 outbreaks. We evaluated multiple models, including logistic regression, random forest, XGBoost, and LSTM neural networks, across classification and regression tasks. XGBoost achieved the highest accuracy for influenza outbreak detection, while LSTM networks outperformed other models in forecasting COVID‑19 case counts, particularly for longer-term predictions. Feature analysis revealed that social media indicators, environmental conditions, and policy measures significantly enhanced predictive performance. The results demonstrate that multimodal machine learning models can provide early warnings, inform resource allocation, and support data-driven decision-making in the US public healthcare system. Our findings highlight the potential of integrating diverse real-time data streams with advanced machine learning techniques to strengthen epidemic preparedness and response.

Infectious disease prediction, machine learning, real-time epidemiological data, social media analytics, influenza, COVID 19, public health forecasting

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