Epidemic outbreaks pose significant threats to global health, economic stability, and social systems, as evidenced by recent pandemics. Timely prediction and early intervention are critical for mitigating their impact. This paper proposes a comprehensive model that integrates Artificial Intelligence (AI) and Big Data to predict epidemic outbreaks with enhanced accuracy and speed. The model combines heterogeneous data sources, including social media trends, electronic health records (EHRs), environmental sensors, mobility patterns, and genomic surveillance, to detect early warning signals of potential outbreaks. The proposed model is structured into four core components: data aggregation, preprocessing and transformation, predictive modeling, and actionable insights generation. Data aggregation involves collecting large-scale, real-time data from diverse platforms. Preprocessing ensures data quality through cleaning, normalization, and feature engineering. Predictive modeling uses advanced AI techniques such as deep learning, natural language processing (NLP), and spatiotemporal analytics to identify patterns and correlations indicative of emerging epidemics. The model outputs are translated into actionable insights for public health officials, enabling proactive responses and targeted resource allocation. This integration enhances situational awareness, allowing for dynamic modeling of disease spread based on factors such as population density, travel behavior, and climatic conditions. Furthermore, the model supports adaptive learning, continuously improving its predictions through feedback from new data. Emphasis is placed on data privacy, ethical use of AI, and cross-sector collaboration to ensure responsible and effective deployment. The implementation of this model could significantly improve epidemic preparedness and response strategies by providing timely alerts, improving surveillance systems, and guiding public health interventions. It is particularly relevant for low-resource settings, where early detection can substantially reduce disease burden. This approach aligns with global health goals by promoting data-driven, preventive public health practices. Future research will focus on validating the model in real-world scenarios, ensuring scalability across regions, and refining algorithms to handle data volatility during crises.