To gain a deeper understanding of the deformation trend of No. 1 toppling deformed slope in the Huangdeng Hydropower Station, the LMBP neural network and the SVR were used to conduct the deformation prediction. Based on the practical deformation monitoring data of the toppling deformed slope in the Huangdeng Hydropower Station, this study analyzed the data of displacement, rainfall, reservoir water level, temperature, etc. Then the reservoir water level, rainfall, temperature, and time were taken as input parameters and the displacement was used as the output parameter to construct the LMBP neural network model and the SVR model. Two models were trained by a part of the monitoring data, and the subsequent monitoring data was used for verification and forecasting, which predicted the deformation of the measuring point in advance. The results show that the accuracy of the two models is higher, the maximum error of the LMBP neural network model is 2.53% and the maximum error of the SVR model is 4.35%, which demonstrate that two prediction methods are both effective.