The continuous advancement of non-destructive testing technology has led to a surge in research on the application of hyperspectral imaging (HSI) technology within the meat products industry. HSI is capable of precisely identifying physical and chemical indicators such as salt, moisture, protein, and fat in dry-cured ham, including the coefficient of determination for salt content and the prediction error of water activity. It also evaluates sensory indicators like viscosity, color, and marbling, including the visualization and classification accuracy of marble patterns. Additionally, it can identify surface mold species with a sensitivity of 85% and a specificity of 86% for distinguishing ochratoxin A-producing bacteria from non-producing bacteria. The unique advantage of HSI which ‘fuses spectral and spatial information’ has enabled the development of various data processing models, such as partial least squares regression and support vector machine-discriminant analysis. These models allow for the simultaneous detection of multiple indicators and the visualization of component spatial distribution, offering an intuitive basis for quality control. This paper summarizes the progress of HSI technology in the quality inspection of dry-cured ham, analyzes its potential for industrial online inspection, including issues such as fat interference, and provides a foundation for future quality identification, optimization, and standardization of dry-cured ham.