Integration of AI and Machine Learning in Wearable Health Devices: A Paradigm Shift in Personalized Healthcare

The convergence of Artificial Intelligence (AI), Machine Learning (ML), and wearable health devices has heralded a transformative era in personalized healthcare. Wearable devices such as smartwatches, fitness trackers, and biosensors are increasingly equipped with intelligent algorithms that can analyze physiological data in real-time, enabling early diagnosis, continuous monitoring, and predictive analytics. This paper reviews the current state of AI/ML integration in wearable health technology, highlighting advancements, architectures, applications, challenges, and future directions. Special emphasis is placed on the role of supervised and unsupervised learning models in disease prediction, anomaly detection, and user behavior modeling. The study concludes with recommendations for improving model accuracy, ensuring data privacy, and addressing ethical concerns.