<p>Digital Image Correlation (DIC) is an advanced technique extensively used for measuring material deformation. This paper reviews the current applications of DIC in rock mechanics testing and explores its integration with deep learning algorithms, based on an extensive literature review. Rock mechanics tests are categorized into five types, with a comprehensive analysis of recent applications of DIC technology across these categories. The results indicate that DIC is predominantly utilized in studies focused on the evolution of surface strain fields and crack propagation in conventional and fracture mechanics tests of rocks, while its use in rheological mechanics remains limited. Deep learning methods in conjunction with DIC for rock mechanics analysis are typically classified as direct or indirect approaches. Deep learning-enhanced DIC addresses two primary challenges of traditional DIC: the subjective influence of parameter selection and the low accuracy of high spatial frequency displacement fields. However, further optimization is required regarding the quality of training datasets and model architecture. The ultimate aim is to simplify parameter selection and continuously enhance performance.</p>