<p>The characteristics of the tongue surface and sublingual vein patterns provide valuable insights into an individual’s health status and have long served as the cornerstone of traditional tongue diagnosis. As a non-invasive digital biomarker, tongue imaging has recently gained attention as a promising modality for capturing internal physiological and pathological variations, with the potential to support remote healthcare delivery and continuous health monitoring. Nevertheless, conventional practice remains highly dependent on subjective clinical judgment, which often introduces variability in diagnostic accuracy and therapeutic decision-making. To mitigate these limitations, computerized tongue image analysis (CTIA) has been developed to enhance objectivity, reproducibility, and consistency. This review proposes a structured taxonomy of CTIA, encompassing the essential stages of image acquisition, preprocessing, dataset construction, feature extraction, and disease detection. By systematically synthesizing advances across these stages, we delineate key challenges and outline potential solutions, particularly regarding data standardization and feature quantification. The taxonomy is intended to provide a coherent framework that may contribute to improving diagnostic precision and reliability, thereby informing the gradual clinical integration of tongue imaging as a supportive tool for non-invasive disease screening.</p>