Real-time remote health monitoring provides valuable physiological data within home environments, particularly benefiting elderly individuals and patients with chronic conditions such as cardiovascular disease or stroke. By enabling continuous tracking, these systems help reduce hospital visits and associated healthcare costs. AI-driven electrocardiograms (AI-ECGs) play a crucial role in improving diagnostic accuracy and enhancing the management and treatment of patients’ health issues. Advanced technologies, including AI-ECG sensors, facilitate the collection and transmission of electrical signals. A programmed processor receives IoT-based ECC sensor signals and employs Radio Frequency Identification (RFID) to uniquely identify and track patients, significantly improve diagnostic precision and treatment effectiveness. This research aims to design and develop real-time IoT-cloud-based RFID and AI-ECG sensor applications for home health monitoring systems, providing secure, continuous, and remote patient care. It further proposes an AI-ECG monitoring system architecture that utilizes IoT-cloud technology to enhance remote health monitoring efficiency. The model integrates RFID and IoT-based sensors to track patients and monitor AI-ECG signals, offering an efficient way to automate and simplify healthcare management tasks. These capabilities include locating patients, managing medical equipment, and streamlining operations within remote home environments. Based on the analysis presented, this study details the development and implementation of Real-time IoT-cloud integrated RFID and AI-ECG systems for Home Health Monitoring Systems (RAESAHHMS), aiming to improve patient care—particularly for individuals with cardiac conditions. Additionally, the incorporation of asymmetric cryptography (using ECC) further enhances the security of real-time remote RAESAHHMS applications to ensure privacy and data protection.