To achieve precise prediction of tailings dam displacement and address existing issues such as rough data preprocessing, inaccurate feature selection, suboptimal model parameters, and poor prediction performance of combined models, a novel prediction model integrating the rime optimization algorithm (RIME) and the convolutional neural network (CNN)-Attention-BiLSTM model was proposed. Firstly, the cumulative displacement of the dam body was decomposed into trend and fluctuation displacements using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. The trend displacement was then predicted using the improved Gaussian process regression (IGPR) model. Subsequently, four key feature factors were selected through a feature selection method combining random forest (RF) and Spearman correlation, and these factors were used as the input for the CNN-Attention-BiLSTM model optimized by RIME parameters, with fluctuation displacement as the output. Finally, the cumulative displacement of the tailings dam was obtained by superimposing the trend prediction results from IGPR and the fluctuation prediction results from the RIME-CNN-Attention-BiLSTM model. The model was validated using a tailings dam in Yunnan Province, China. The prediction results show that the coefficient of determination (R²) is 0.997, the mean absolute error (MAE) is 0.546 mm, and the root mean square error (RMSE) is 0.628 mm, indicating a high degree of consistency with the actual values and minimal error. These results demonstrate that the proposed method has high prediction accuracy and can significantly improve the prediction of complex displacement changes in the dam body, providing early warnings and decision-making support for tailings dam safety.