Smart Healthcare and Wellness Systems for Real-Time Monitoring

Innovations in intelligent healthcare and the Internet of Medical Things (IoMT) are transforming medical practice, moving it from traditional reactive treatment toward preventive, real-time, and personalized care. This research presents an Edge–Cloud Healthcare Framework that brings together information from a number of sources, including wearable sensors, biomedical imaging equipment, and environmental monitoring systems. Fundamentally, the framework employs a Hybrid Deep Neural Network (HDNN) that integrates convolutional layers for image-based medical Recurrent layer analysis for interpreting time-series physiological signals. Early adoption is in favor this design. anomaly detection and effective risk prediction. In order to balance the computational burdens, a Dynamic Priority Scheduling Algorithm (DPSA) is applied, To maintain equilibrium among the computational demands and cloud resources based on latency requirements and the criticality of patient data, ensuring fast responses alongside accurate results. evaluation of an experiment utilizing 5G-enabled ECG and SpO2wearable Devices' inference latency was average of 14.7 ms and improved diagnostic accuracy by 9.3% over conventional telemedicine models. Moreover, smart home simulations demonstrated the task that is adaptive orchestration less energy consumption by 18% while remaining receptive. In general, the results show that combining edge computing with intelligent arranging mechanisms is able to provide scalable, reliable, and patient-focused healthcare solutions, enabling both ongoing observation and personalized wellness interventions in real-world environments.