The methodology traditionally used to analyse the quality parameters of meat and meat products involves the destruction of the samples. Moreover, these techniques are tedious, time and solvent consuming. As an alternative, the use of different techniques based on images are being evaluated to be proposed as methods to analyse the quality parameters of meat and meat products non-destructively. For that, this study reviews the use of MRI to analyse meat and meat products, being specially centered on the used procedures and main achievements. High-Field and Low-Field MRI scanners have been used for analysing meat products. These studies have been mainly focused on classification purposes and prediction of different quality parameters of hams and loins. The usual procedure consists of three steps: image acquisition, image analysis, and data analysis. The most applied methodology have been SE T1, texture algorithms, and classification (PCA, PLS) and prediction (MLR) techniques, respectively. MRI publications have shown accurate results, indicating the appropriateness of this technique to be proposed as an alternative methodology to analyse meat and meat products with high reliability in a non-destructive way. However, improvements in the image acquisition and analysis as well as in the process automation are desired.