This review explores the integration of machine learning (ML) with wearable biomedical devices for personalized health monitoring. The study classifies wearable devices into three categories: bio-electrical (ECG, EEG, EMG), electro-chemical and bio-impedance (glucose, electrolytes), and electro-mechanical (motion, physical activity). ML algorithms enhance these devices by analyzing physiological data to detect anomalies, predict health conditions, and provide personalized recommendations. The review highlights applications in Parkinson’s disease management, rehabilitation, cardiovascular health, mental health, and sleep monitoring. For example, ML models classify ECG signals for blood pressure estimation, EEG for emotion recognition, and EMG for gesture control. Bio-electrical wearables also enable real-time stress detection, panic attack prediction, and seizure forecasting. Electro-chemical and bio-impedance devices monitor glucose levels, sweat biomarkers, and bladder fullness. Electro-mechanical wearables track gait, motion, and posture, aiding in fall detection and rehabilitation. The review emphasizes the potential of ML in improving healthcare through early detection, personalized interventions, and continuous monitoring. It also discusses the growing trend of personalized wearable devices, with over 78% of studies incorporating personalized features. The integration of ML with wearables offers promising solutions for chronic disease management, mental health, and personalized lifestyle recommendations. The review concludes that ML-driven wearable devices are transforming healthcare by enabling real-time, individualized health insights and interventions.This review explores the integration of machine learning (ML) with wearable biomedical devices for personalized health monitoring. The study classifies wearable devices into three categories: bio-electrical (ECG, EEG, EMG), electro-chemical and bio-impedance (glucose, electrolytes), and electro-mechanical (motion, physical activity). ML algorithms enhance these devices by analyzing physiological data to detect anomalies, predict health conditions, and provide personalized recommendations. The review highlights applications in Parkinson’s disease management, rehabilitation, cardiovascular health, mental health, and sleep monitoring. For example, ML models classify ECG signals for blood pressure estimation, EEG for emotion recognition, and EMG for gesture control. Bio-electrical wearables also enable real-time stress detection, panic attack prediction, and seizure forecasting. Electro-chemical and bio-impedance devices monitor glucose levels, sweat biomarkers, and bladder fullness. Electro-mechanical wearables track gait, motion, and posture, aiding in fall detection and rehabilitation. The review emphasizes the potential of ML in improving healthcare through early detection, personalized interventions, and continuous monitoring. It also discusses the growing trend of personalized wearable devices, with over 78% of studies incorporating personalized features. The integration of ML with wearables offers promising solutions for chronic disease management, mental health, and personalized lifestyle recommendations. The review concludes that ML-driven wearable devices are transforming healthcare by enabling real-time, individualized health insights and interventions.