Abstract: Surface subsidence and slope sliding caused by mining operations pose significant threats to mine safety and the surrounding environment. How to efficiently monitor and accurately invert the deformation mechanism has become an urgent problem to be solved. Interferometric Synthetic Aperture Radar （InSAR） has gradually become an important technical means in the field of mining deformation monitoring due to its all-weather, large-scale, and high-precision monitoring capabilities. This paper systematically reviews the research progress of InSAR technology in mine monitoring, with a focus on analyzing one-dimensional, two-dimensional, and three-dimensional displacement monitoring techniques and their application effects in mining deformation monitoring. This paper discusses the existing deformation monitoring methods based on single track, multi-track, and multi-sensor InSAR data, as well as the displacement reconstruction technology that integrates InSAR and deformation models, in response to the demand for displacement monitoring in different dimensions of mining deformation monitoring. Additionally, it delves into the mechanical parameter inversion technology based on InSAR data, including the application of the PIM model and its extended models in mechanical parameter inversion, and summarizes the role of optimization methods such as Genetic Algorithm （GA） and Simulated Annealing （SA） in improving the accuracy of parameter inversion. This paper raises the issue of the lack of universal research and model validation on complex mining areas and long-term monitoring situations in existing studies. At the same time, it looks forward to the significant potential of cross-technology integration and machine learning technology in enhancing the accuracy and intelligence level of InSAR monitoring. This paper aims to provide theoretical support and technical references for mine slope monitoring and promote the further development of InSAR technology in mine safety management.