Article’s

The global convergence of cloud computing and machine learning (ML) has catalyzed a paradigm shift in the delivery and management of modern healthcare. Traditional hospital centric models are progressively giving way to proactive, data-driven, remote patient monitoring systems that can detect physiological anomalies in real time, thereby reducing the burden on overwhelmed healthcare infrastructure and enabling preventive rather than reactive medical intervention. This is particularly critical in the context of the United Nations Sustainable Development Goal 3 (SDG 3: Good Health and Well-being), which demands scalable, equitable, and technologically advanced solutions for healthcare delivery worldwide. Despite significant advancements in wearable biosensor technology and cloud architectures, existing remote health monitoring systems frequently suffer from latency in anomaly detection, insufficient predictive capability for chronic disease progression, and inadequate data security frameworks for managing sensitive patient biometrics. Lightweight edge-cloud hybrid models are often either too computationally shallow for accurate multi-parameter disease prediction or too architecturally heavy for deployment on resource-constrained IoT medical devices. To address these critical limitations, this paper proposes a highly optimized, cloud-based smart healthcare monitoring framework that synergizes real-time data acquisition from wearable IoT biosensors with the predictive power of ensemble machine learning algorithms deployed on a scalable cloud backend. The system continuously monitors key vital signs including heart rate, blood oxygen saturation (SpO2), blood pressure, body temperature, and electrocardiogram (ECG) signals. Anomalies and disease risk patterns are detected using a hybrid ML pipeline combining Random Forest classifiers with Long Short-Term Memory (LSTM) networks for temporal pattern recognition. Furthermore, this research systematically synthesizes 25 pivotal studies, mapping the evolutionary trajectory of healthcare monitoring from rudimentary telemetry systems to modern ML-powered predictive analytics platforms. Empirical evaluations on benchmark medical datasets demonstrate that the proposed ensemble classifier achieves an exceptional classification accuracy of 96.8% with a sensitivity of 95.4% and specificity of 97.2% for critical cardiac events. The system sustains an average alert latency of under 1.2 seconds from anomaly detection to clinician notification, decisively satisfying real-time health surveillance requirements. This framework ultimately provides a scalable, privacy-compliant, and economically accessible blueprint for the global deployment of AI-augmented preventive healthcare ecosystems.