The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of healthârelated data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learningâreinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learningâreinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learningâreinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in realâtime monitoring, outâofâclinic diagnosis, and onsite food safety detection are proposed.