The Internet of Medical Things (IoMT) has emerged as a transformative technology in healthcare, enabling continuous, real-time monitoring of patients' health through interconnected medical devices and sensors. The integration of Machine Learning (ML) techniques within IoMT frameworks has the potential to significantly enhance remote health monitoring by providing advanced predictive analytics, early diagnosis, and personalized treatment plans. This paper presents a comprehensive study on the synergistic application of IoMT and ML for remote health monitoring. The proposed system architecture leverages wearable devices and sensors to collect a wide range of health data, which is then transmitted to a centralized platform where ML algorithms process and analyze the information. Key components of the system include data acquisition, preprocessing, feature extraction, and the implementation of various ML models to identify patterns and predict potential health issues. Experimental results demonstrate the efficacy of the proposed system in accurately monitoring health parameters and predicting medical conditions with high precision. The discussion includes a comparison with existing systems, highlighting the improvements in performance and reliability. The paper concludes by addressing the limitations of the current study and proposing future research directions to further enhance the integration of IoMT and ML, aiming to revolutionize remote health monitoring and improve patient outcomes.