From data to diagnosis: A comprehensive review of machine learning-driven wearable sensors in healthcare.

The integration of machine learning (ML) with advanced wearable sensor technologies is revolutionizing healthcare by enabling real-time, intelligent monitoring of physiological parameters such as electrocardiogram (ECG), blood glucose, and respiratory patterns. This review systematically examines the transformative potential of ML-driven biosensors across three core domains: health monitoring, early disease detection, and precision medicine. Key technological advancements-including self-optimizing sensor networks, explainable AI (XAI) architectures, and edge-computing-enabled miniaturized devices-are critically evaluated. Despite rapid progress, the translation of these technologies into clinical practice faces significant challenges, such as data standardization, algorithmic interpretability, privacy concerns, and regulatory hurdles. This paper also discusses emerging trends, including federated learning, quantum machine learning, and neural interfaces, which hold promise for overcoming these barriers. By addressing these challenges and leveraging ongoing interdisciplinary collaborations, ML-enhanced wearable systems are poised to redefine personalized medicine and proactive healthcare delivery on a global scale.