This paper introduces a pioneering smart ECG monitoring system, propelled by an Artificial Intelligence (AI)-assisted arrhythmia detection paramedical application. The system is underpinned by a Center-Edge Computing (IoT) framework, encompassing sophisticated hardware, firmware, and web services. A pivotal feature is the monitoring patch, capable of sampling ECG data at 256 Hz with 16-bit precision. Bluetooth connectivity is employed for energy-efficient wireless data transfer, facilitating real-time monitoring without disrupting daily activities. The device’s power efficiency is further enhanced through the strategic use of switching converters, ultra-low-power components, and firmware-optimized intermittent operation. The software architecture comprises an Edge Arrhythmia Detection application and a centralized ECG analysis platform, complete with streaming and monitoring capabilities. A hallmark of our innovation is the integration of an extended time factor for arrhythmia diagnosis, significantly bolstering the system’s diagnostic accuracy. Validation on a test dataset revealed impressive accuracy rates: 98.7% using artificial neural networks and K-nearest neighbor methods, and 98.1% utilizing decision tree algorithms.