The growing demand for meat products has made quality and safety assessment a priority throughout production, processing, storage, distribution, and consumption. Meat quality encompasses sensory attributes, physicochemical properties, nutritional composition, microbial status, freshness, and safety-related characteristics. Conventional sensory, physicochemical, and microbiological methods provide reliable reference measurements, but they are generally time-consuming, labor-intensive, destructive, and unsuitable for real-time or online inspection. This review summarizes recent advances in non-destructive technologies for meat quality assessment, including spectroscopy, imaging and machine vision, odor and taste sensing, ultrasonic and electrical methods, low-field nuclear magnetic resonance, and multi-source information fusion. After outlining the fundamental principles and representative applications of each technology, this review further compares the types of sensing information they provide, the quality attributes they are suitable for assessing, their application scenarios, practical limitations, and model validation requirements. Current evidence indicates that these technologies have considerable potential for rapid screening, process monitoring, quality visualization, and intelligent evaluation. However, practical deployment still requires reliable reference measurements, representative calibration datasets, robust models, external validation, and standardization across instruments and production conditions. Future research should prioritize sensor miniaturization, calibration transfer, standardized datasets, explainable intelligent evaluation, and practically deployable multi-source fusion.