Monitoring the deformation of rock surrounding deep coal mine roadways is critical for operational safety, yet conventional methods are often inefficient and lack the precision to capture complex geomechanical behavior. To address this challenge, we developed and validated a high-precision analysis workflow utilizing 3D laser scanning. This methodology integrates a multi-stage point cloud filtering process with a hybrid Principal Component Analysis and Iterative Closest Point (PCA-ICP) algorithm for high-fidelity registration of multi-temporal datasets, enabling deformation analysis via cloud-to-cloud (C2C) distance calculations. Applied to the No. 20105 belt conveyor roadway in the Dahaize coal mine, our method achieved a registration root mean square error (RMSE) of only 0.0136 m. The analysis revealed a distinct pattern of asymmetric deformation; while the roof and floor remained stable, the right wall exhibited significant convergence, with displacement in the lower section being substantially greater than in the upper section. This study establishes a robust methodology capable of rapidly generating comprehensive, centimeter-scale 3D deformation maps for entire roadway sections, providing a timely and quantitative basis for evaluating support performance, forecasting geohazard risks, and optimizing stability control in deep mining operations.