Abstract: The quality and safety of agricultural products are fundamental to ensuring food safety and promoting the sustainable development of the agricultural economy. During harvesting, transportation, storage, and processing, agricultural products are susceptible to various types of damage, including mechanical damage, freezing injury, over-maturity, and microbial spoilage, which can result in structural degradation, changes in nutritional composition, and deterioration of edible quality. Therefore, the development of rapid, accurate, and non-destructive detection methods for agricultural product damage is of great significance for evaluating quality status and ensuring supply chain safety. Conventional non-destructive detection methods have been widely applied in agricultural product quality assessment. However, they still face several limitations, including relatively high detection costs, limited penetration capability of optical signals, and the fact that some technologies mainly rely on volatile compounds for quality evaluation. Therefore, the development of highly sensitive and deep-level non-destructive detection techniques for monitoring internal tissue changes in agricultural products remains an important research direction. In recent years, electrochemical impedance spectroscopy (EIS) has attracted increasing attention due to its capability of acquiring electrical response information from biological tissues through alternating current excitation and reflecting structural changes in agricultural products at the cellular level, thereby providing a promising approach for damage detection of agricultural products. This review systematically integrates the detection parameters and modeling approaches of EIS for seven categories of agricultural product damage, including mechanical damage, freezing injury, heat injury, microbial spoilage, dehydration shrinkage, over-maturity, and meat spoilage, and clarifies its technical applicability and practical limitations. The basic principles of impedance detection are introduced, with emphasis on the selection of equivalent circuit models, electrode types, measurement frequencies, and impedance data processing methods. Furthermore, the effects of different detection conditions on impedance signal acquisition and model performance are analyzed. The application progress of EIS in different damage detection scenarios is summarized, and the relationships among damage-induced tissue structural changes, electrical response characteristics, and detection model construction strategies are discussed, providing theoretical support for further application of EIS technology in agricultural product quality assessment. Finally, the key challenges restricting the practical application of EIS technology are analyzed, including electrode variability, individual differences among agricultural products, miniaturization of detection devices, and the establishment of quality databases. Future development directions are also discussed. The standardization of electrode structures and measurement parameters, the development of portable detection devices suitable for complex environments, and the integration of multi-source data analysis methods are considered important directions for promoting the transformation of EIS technology from laboratory research to practical applications. With continuous advances in impedance measurement technologies, sensor technologies, and machine learning algorithms, the application scope of EIS in agricultural product damage detection and quality monitoring will be further expanded. This progress will promote the development of detection methods toward rapid, accurate, and intelligent evaluation, while facilitating standardized measurement protocols, reliable data interpretation, and large-scale quality monitoring across different agricultural products and application scenarios, thereby providing theoretical references and technical support for agricultural product quality assessment, postharvest quality control, and the improvement and practical implementation of intelligent detection technologies.