This review explores the integration of artificial intelligence (AI) and personalized health monitoring through wearable devices, categorizing them into three main types: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, leveraging machine learning to extract meaningful insights from extensive datasets. The bio-electrical category includes devices that use biosignal data such as ECGs, EMGs, and EEGs to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals like glucose levels and electrolytes, providing a holistic understanding of the wearer's physiological state. The electro-mechanical category encompasses devices designed to capture motion and physical activity data, offering valuable insights into an individual's physical activity and behavior. The review critically evaluates the integration of machine learning algorithms within these wearable devices, highlighting their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and personalized lifestyle recommendations, the paper outlines how advanced machine learning techniques combined with wearable devices can lead to more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being. The literature review is structured into three main sections: bio-electrical wearable devices, bio-impedance and electro-chemical wearables, and electro-mechanical wearables. Each section delves into the specific applications and advancements in these areas, showcasing the potential of personalized wearable devices in managing various health conditions and improving patient outcomes. The review concludes by emphasizing the growing scholarly interest in personalized wearable devices and the potential of this technology to transform healthcare.This review explores the integration of artificial intelligence (AI) and personalized health monitoring through wearable devices, categorizing them into three main types: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, leveraging machine learning to extract meaningful insights from extensive datasets. The bio-electrical category includes devices that use biosignal data such as ECGs, EMGs, and EEGs to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals like glucose levels and electrolytes, providing a holistic understanding of the wearer's physiological state. The electro-mechanical category encompasses devices designed to capture motion and physical activity data, offering valuable insights into an individual's physical activity and behavior. The review critically evaluates the integration of machine learning algorithms within these wearable devices, highlighting their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and personalized lifestyle recommendations, the paper outlines how advanced machine learning techniques combined with wearable devices can lead to more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being. The literature review is structured into three main sections: bio-electrical wearable devices, bio-impedance and electro-chemical wearables, and electro-mechanical wearables. Each section delves into the specific applications and advancements in these areas, showcasing the potential of personalized wearable devices in managing various health conditions and improving patient outcomes. The review concludes by emphasizing the growing scholarly interest in personalized wearable devices and the potential of this technology to transform healthcare.