The increasing aging population and rising prevalence of chronic illnesses necessitate innovative healthcare solutions to address the growing demand for continuous and efficient patient monitoring. This paper proposes an Artificial Electronic Caregiver (AEC) system that integrates Internet of Things (IoT) technology and machine learning algorithms to enable real-time health monitoring and early detection of health anomalies. The AEC system utilizes a network of sensors to collect critical health data, including heart rate, blood pressure, and body temperature. These data points are processed and analyzed through advanced machine learning techniques to identify irregularities and generate actionable insights. The proposed system offers several advantages over traditional healthcare approaches. It ensures continuous real-time monitoring, facilitating timely medical intervention. Additionally, it supports remote healthcare services, minimizing the necessity for frequent hospital visits, and promotes a personalized healthcare experience through data-driven customized care plans. The paper elaborates on the architecture of the AEC system, detailing key components such as IoT sensors, data analytics platforms, and user interfaces. A pilot study conducted with elderly individuals and patients with chronic illnesses validates the system's feasibility, reliability, and effectiveness. The results demonstrate the AEC system's capability to deliver early detection and preventive care, significantly enhancing healthcare outcomes and improving the quality of life for patients and caregivers. This research contributes to the advancement of IoT-based healthcare solutions, offering a scalable and efficient model for future implementations.