Effective disease surveillance and outbreak response are high public health priorities, particularly in resource-limited healthcare systems facing significant challenges with data collection, predictive modeling, and coordinated intervention strategies. It hence proposes an integrated AI-driven framework responding to these challenges and strengthening the capacity of public health authorities to better manage infectious disease outbreaks. This framework will integrate real-time data from different sources such as electronic health records, syndromic surveillance, environmental indicators, and social media in a harmonious way into one centralized repository for the enablement of advanced predictive modeling techniques. It mainly comprises a disease forecasting model, an outbreak detection algorithm, and an intervention optimization tool integrated to provide early warning, accurate prediction, and data-driven decision support. An extensive performance evaluation of the framework was conducted using accuracy, sensitivity, specificity, and area under the ROC curve as some of the metrics. The results are very satisfactory, while the overall accuracy is 94.8% (95% CI: 93.2%–96.4%) with an AUC-ROC of 0.967 (95% CI: 0.951–0.983). Comparative performance analysis using state-of-the-art deep learning architectures further establishes the superiority of the proposed hybrid model. Transparency and explainability in the framework were guaranteed up to an extent that allows understanding the models and decision-making process by the public health authorities.