Abstract: Advanced patient monitoring systems are now more important than ever due to the changing healthcare landscape and the increasing requirement for prompt and effective patient care. Conventional approaches frequently fail to provide prompt reactions to significant shifts in a patient's condition. In this regard, the incorporation of Internet of Things (IoT) technology allows for ongoing, real-time data collecting from remote and wearable medical devices, providing a more responsive and dynamic healthcare setting. Machine learning techniques are used in the system to efficiently analyse large streams of health data and to identify minor trends and anomalies that could be early indicators of deterioration. A proactive healthcare approach is supported by this sophisticated anomaly detection, which enables healthcare providers to act quickly and enhance patient outcomes. Ultimately, the combination of machine learning and IoT gives healthcare professionals predictive insights that enable quicker, more intelligent, and more individualised patient treatment.

Keywords: ECG monitoring, machine learning, mobile health, real-time analysis, arrhythmia detection, wearable devices, healthcare technology

| DOI: 10.17148/IARJSET.2025.12533