Epidemics continue to pose significant threats to public health and economic stability worldwide. The rapid transmission of infectious diseases calls for timely and effective interventions, underscoring the need for advanced predictive systems. This paper proposes an integrated framework that combines real-time data streams, artificial intelligence (AI) forecasting models, and business analytics (BA) tools to enhance epidemic preparedness. By harnessing diverse data-from epidemiological records and environmental sensors to mobility patterns and social media trends-the system aims to deliver accurate predictions and actionable insights. AI techniques, including Random Forests, Support Vector Machines, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, are deployed to identify early outbreak signals, while BA methods such as regression analysis, Monte Carlo simulations, and linear programming help evaluate intervention strategies and optimise resource allocation. The integration of real-time data ensures the model remains adaptive and robust, while the use of explainability frameworks enhances transparency. The results of extensive simulations and stakeholder evaluations suggest that this integrated approach can substantially improve epidemic response by supporting evidence-based decisions and reducing the economic and health impacts of outbreaks. The paper concludes with recommendations for further research and outlines a roadmap for real-world implementation.