<p>This paper presents a novel Supervisory Control and Data Acquisition–inspired architecture for intelligent healthcare monitoring that combines real-time biosignal acquisition, edge-based artificial intelligence inference, and federated learning to support scalable and privacy-preserving patient risk assessment. Drawing inspiration from industrial supervisory control systems, the proposed framework introduces a modular and interpretable design for processing multimodal health data—including vital signs, laboratory results, and wearable sensor streams—to detect transitions in patient risk states. The system integrates Bayesian networks for causal feature selection, Markov chains for temporal risk stratification, and lightweight neural networks for predictive inference. Clinical alerts are triggered using interpretable rule-based logic; for example, when the fraction of inspired oxygen falls below 21% and the Glasgow Coma Scale score is less than 8—thresholds consistent with intensive care intervention guidelines. This logic yields an alert rate of approximately 26.5%, matching typical clinical escalation frequencies. Deployed on low-power edge devices, the proposed system supports federated learning, enabling decentralized model training across hospital nodes without exposing patient data. Experiments on a structured intensive care dataset demonstrate 80% classification accuracy, an area under the receiver operating characteristic curve of 0.8779, and robust real-time performance. The architecture offers transparency, responsiveness, and compliance with data protection regulations, bridging the gap between industrial-grade supervisory control and trustworthy artificial intelligence for modern clinical care.</p>