The conflict between coal resource extraction and ecological environmental protection is particularly pronounced in the Gaojialiang coal mine of Inner Mongolia. To accurately characterize the deformation extent and evolutionary patterns of mining-induced ground subsidence within the study area, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology combined with Sentinel-1 radar remote sensing data were utilized to obtain the annual average deformation velocity and time-series cumulative deformation over three primary panels. Additionally, the Kriging interpolation method was employed to predict and supplement data in decoherence regions, ensuring comprehensive coverage of the deformation field. The results show that three distinct subsidence zones are identified, spatially correlated with the mined-out areas of panels 203, 301, and 401, respectively. The subsidence is characterized by slow deformation, with a peak annual average deformation velocity of approximately -34 mm/a. The temporal initiation and spatial propagation of subsidence in the three panels align closely with the actual mining sequence and operational conditions. Among these, panel 401 exhibited the largest subsidence area, covering approximately 4.56 km², with a maximum cumulative deformation of -189 mm, followed by panels 301 and 203 in descending order. Ground fractures identified through high-resolution optical remote sensing imagery are consistent with field investigations, predominantly distributed in the zones of maximum deformation intensity. Based on the deformation characteristics and fractures distribution, three high-risk geohazard zones are delineated within the study area. The primary driver of ground subsidence is attributed to longwall mining activities, while geological structures and precipitation infiltration also contributed to the deformation process. SBAS-InSAR technology has good application effects in monitoring large-scale mining-induced ground subsidence, and can provide crucial technical and data support for geological disaster prevention and ecological environment restoration in Gaojialiang mining area.