Side-slope deformation monitoring compares monitoring data from the same area over different periods and measures the deformation variables. Because of the gaps and coarseness of side-slope monitoring data, a side-slope monitoring method that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV)–based photogrammetry point clouds is proposed, aiming to solve the problem of slope monitoring in complex scenes. First, TLS and UAV-based photogrammetry point clouds are acquired. Then, the two types of point clouds are registered by an iterative closest point algorithm. Next, the data gap areas in the TLS point cloud are detected, and a gap-filling method is used to integrate the UAV-based photogrammetry point cloud with the TLS point cloud. Finally, side-slope deformation is detected based on a multiscale model-to-model cloud comparison algorithm. A side slope in Chenggong, Kunming, China, is taken as an example. The surface deformation of the side slope was monitored during January and June 2021. The experimental results show that the registration errors of the two-phase integration point cloud are 0.039 m and 0.035 m. The root mean square errors of the four ground checkpoints are 0.033 m and 0.038 m. Finally, the side slope is found to have deformed and formed a main deformation area, which shows that this side slope was in an active state.