Smart wearable sensors, supported by machine learning and innovative intelligent materials, enable rapid and accurate disease diagnosis, personalized treatment, and continuous health monitoring without disrupting daily life. This integration facilitates a shift from traditional, hospital-centered healthcare to a more decentralized, patient-centered model, where wearable sensors can collect real-time physiological data, perform in-depth analysis on these data streams, and generate actionable insights for accurate diagnosis and personalized treatment at the point of care. Although intelligent materials, machine learning, and wearable sensing technologies have developed rapidly, there is a lack of comprehensive reviews systematically examining the intersection of these fields. This review addresses this gap by critically analyzing wearable sensing technologies supported by intelligent advanced materials and artificial intelligence. It first studies the state-of-the-art smart materials that enhance sensor functionality, including self-healing, metamaterials, and responsive materials. Advanced machine learning methods integrated into wearable devices are discussed, emphasizing their roles in biomedical applications. Additionally, the review examines the combined impact of wearable sensors supported by intelligent materials and machine learning, as well as their applications in smart diagnosis and treatment. Finally, existing challenges are addressed, including technical and compliance issues, information security concerns, and regulatory considerations, while future directions for advancing smart healthcare are proposed.