Hyperspectral imaging (HSI) is an emerging technology that uses imaging and spectroscopy to obtain spatial and spectral information for each pixel in a food sample. This technique enables comprehensive and non-destructive analysis of food quality. The HSI technique is quite rapid, robust, reliable, and has a systematic stepwise framework. The methodology consisted of sample preparation, system setup, calibration, illumination setup, image acquisition, data storage, image preprocessing, spectral feature extraction, model development, visualisation, and interpretation. All the steps are quite intricate and should be followed cautiously. Hyperspectral images are captured under controlled conditions, and spatial as well as spectral features associated with different attributes of food quality are generated. Multivariate statistical methods or machine learning algorithms are used to relate spectral features to the quality parameters of foods. It applies to a variety of food products, including fruits, vegetables, dairy products, meats, and grains. These techniques can support quality control in the food industry and provide safe food to consumers.