The integration of the Internet of Things (IoT) and machine learning (ML) has revolutionized remote health monitoring by enabling real-time data collection and advanced abnormality detection. IoT devices, such as wearable sensors and smart medical equipment, collect continuous health data, while ML algorithms analyse this data to detect anomalies and predict potential health risks. This focusing on IoT-enabled remote health monitoring systems with ML-based abnormality detection. The findings highlight advancements in real-time monitoring, predictive analytics, and decision support, emphasizing their potential to improve patient outcomes, reduce healthcare costs, and optimize resource allocation. Challenges such as data privacy, interoperability, and computational efficiency are also discussed, along with future research directions.