Emerging infectious diseases continue to pose significant global threats, exposing critical limitations in traditional surveillance systems that rely on delayed reporting, fragmented data sources, and retrospective analysis. This paper proposes a Real-Time AI Early Warning System (RAIEWS) that leverages multi-modal data integration across clinical health records, human mobility patterns, and environmental signals to enable proactive and predictive outbreak intelligence. The framework introduces a unified architecture that combines spatio-temporal deep learning models, graph-based epidemiological inference, and adaptive anomaly detection to identify early signals of disease emergence with minimal latency. The proposed system employs a hybrid edge-cloud pipeline for real-time data ingestion and processing, enabling continuous monitoring and rapid response across geographically distributed regions. A novel Multi-Modal Epidemiological Intelligence (MMEI) layer is designed to fuse heterogeneous data streams, while a Mobility-Aware Transmission Model (MATM) captures population movement dynamics to improve outbreak forecasting accuracy. Additionally, an Environmental Pathogen Amplification Index (EPAI) is introduced to quantify the influence of climatic and environmental conditions on disease propagation. Experimental evaluation using simulated real-world datasets demonstrates that the proposed approach significantly improves early detection accuracy, reduces response latency, and enhances predictive reliability compared to conventional surveillance systems. The results highlight the system’s ability to detect outbreak precursors days to weeks in advance, offering a critical window for intervention. Furthermore, the integration of privacy-preserving federated learning mechanisms ensures secure and compliant handling of sensitive health data across distributed infrastructures. This research contributes a scalable and intelligent framework for next-generation epidemic intelligence, with practical implications for public health agencies, smart city infrastructures, and global disease monitoring networks.