The validity of evidence value assessment directly affects judicial fairness. The traditional assessment model that relies on expert experience suffers from problems such as subjective bias. The likelihood ratio (LR) based on the Bayesian framework provides an objective measurement of probative force by calculating the probability ratio of evidence under the prosecution and defense hypotheses, and has become a key tool for connecting the technical and legal dimensions. This paper systematically reviews the research status of the LR method in the three major fields: physical evidence, forensic medical examination, and audio-visual material identification. From a technical perspective, the LR method covers feature-based and score-based feature acquisition methods, as well as model construction techniques using classical statistics and artificial intelligence. System performance calibration and evaluation techniques continue to improve, providing dual guarantees for the reliability and accuracy of LR results. Current research shows new trends from experience-driven to data-driven, from single-modal to multi-modal fusion, and from laboratory analysis to real-time intelligent on-site detection. The LR method not only promotes technological updates but also facilitates the transformation of judicial proof concepts to an “experience-data” hybrid model. However, China still faces challenges in database construction, standardized validation, interdisciplinary talent cultivation, and judicial acceptance. This paper aims to provide a theoretical reference for building an interpretable, verifiable, and scalable evidence assessment framework, and to assist in the standardization of forensic science evidence assessment and the practice of judicial fairness in China.