Land subsidence triggered by the overexploitation of groundwater in the Alto Guadalentín Basin (Spain) aquifer system poses a significant geological-anthropogenic hazard. In this work, for the first time, we propose a new point cloud differencing methodology to detect land subsidence, based on the multiscale model-to-model cloud comparison (M3C2) algorithm. This method is applied to two airborne LiDAR datasets acquired in 2009 and 2016, both with a density of 0.5 p/m2. The results show vertical deformation rates up to 10 cm/year in the basin during the period from 2009 to 2016, in agreement with the displacement reported in previous studies. Firstly, the iterative closest point (ICP) algorithm is used in the point cloud registration with a very stable and robust performance. LiDAR datasets are affected by several source of errors related to the construction of new buildings and the changes caused by vegetation growth. The errors are removed by means of gradient filtering and cloth simulation filtering (CSF) algorithm. Other sources of error are related to the internal edge connection error in the different flight lines. To address these errors, the smoothing point cloud method by incorporating average, maximum and minimum cell elevation is applied. LiDAR results are compared to the velocity measured by a continuous GNSS station and an InSAR dataset. For the GNSS-LiDAR comparison, a velocity average from a buffer area processed in the cloud point dataset is applied. For the InSAR-LiDAR comparison a 100m*100m grid is computed in order to assess any similarities and discrepancies. The results show a good correlation between the vertical displacement derived from the three different surveying techniques. Furthermore, LiDAR results have been compared with the distribution of soft soil thickness showing a clear relationship. Detected ground subsidence is a consequence of the evolution of the piezometric level of the Alto Guadalentín aquifer system that has been exploited since the 1960s producing a great groundwater level drop. The study underlines the potential of LiDAR to monitor the range and magnitude of vertical deformations in areas that prone to be affected by aquifer-related land subsidence.