To address the limitations of traditional early warning methods in open-pit slope displacement monitoring—particularly their neglect of spatiotemporal correlations and their difficulty in analyzing multi-scale non-stationary sequences—this study proposes an early warning framework that integrates spatiotemporal clustering with multi-scale decomposition. Taking the southern slope of the Bayan Obo Main Pit as a case study, high-risk deformation zones were identified using DBSCAN-based spatiotemporal clustering applied to slope radar monitoring data. The displacement time series were decomposed using Variational Mode Decomposition (VMD) into trend and periodic components, for which Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models were respectively developed. The results indicate that (1) DBSCAN effectively detects clusters characterized by high average cumulative displacement and broad spatial distribution, while filtering out isolated outliers. (2) The trend component prediction achieved a coefficient of determination (R²) of 0.99755, while the periodic component prediction yielded a root mean square error (RMSE) of just 0.0978 mm. The reconstructed total displacement achieved an R² of 0.9973, verifying the proposed multi-scale decomposition and hybrid modeling framework’s high accuracy and robustness in slope deformation modeling and early warning.