This work introduces a novel method for estimating hydrological loading displacement using 3D Convolutional Neural Networks (3D-CNN). This approach utilizes vertical displacement time series data from 41 Global Navigation Satellite System (GNSS) stations across Yunnan Province, China, and its adjacent areas, coupled with spatiotemporal variations in terrestrial water storage derived from the Gravity Recovery and Climate Experiment satellites (GRACE). The 3D-CNN method demonstrates markedly higher inversion precision compared to conventional load Green's function inversion techniques. This improvement is evidenced by substantial reductions in deviations from GNSS observations across various statistical metrics: the maximum deviation decreased by 1.34 millimeters, the absolute minimum deviation by 1.47 millimeters, the absolute mean deviation by 79.6%, and the standard deviation by 31.4%. An in-depth analysis of terrestrial water storage and loading displacement from 2019 to 2022 in Yunnan Province revealed distinct seasonal fluctuations, primarily driven by dominant annual and semi-annual cycles, and these periodic signals accounted for over 90% of the variance. The spatial distribution of terrestrial water loading displacement is strongly associated with regional precipitation patterns, showing smaller amplitudes in the northeast and northwest and larger amplitudes in the southwest. The research findings presented in this paper offer a novel perspective on the spatiotemporal variations of environmental load effects, particularly those related to the terrestrial water loading deformation with significant spatial heterogeneity. Accurate assessment of the effects of terrestrial water loading displacement (TWLD) is of considerable importance for precise geodetic observations, as well as for the establishment and maintenance of high-precision dynamic reference frames. Furthermore, the development of TWLD model that integrates GRACE and GNSS data provides valuable data support for the higher-precision inversion of changes in terrestrial water storage.