Reservoir water level fluctuations and rainfall drive bank slope deformation, typically exhibiting spatiotemporal lags. Existing prediction models often fail to characterize these complex coupled relationships and rely on manual variable selection, causing information loss and reduced performance. To address these issues, a novel multi-point prediction model for reservoir bank slope deformation based on lag-aware clustering (LAC), referred to as LAC-MOGP, is proposed in this study. First, the Maximal Information Coefficient (MIC) quantifies the lag between influencing factors and deformation for objective factors screening. Next, an improved dynamic time warping (DTW) algorithm with a lag-difference-constrained matching window is combined with affinity propagation (AP) to cluster monitoring points based on asynchronous temporal correlations. Finally, a DTW-based similarity weighting scheme is embedded into a multi-output Gaussian Process (MOGP) kernel to refine covariance modeling and predict deformation within each cluster. Validated using observations from the Jinlongshan slope of the Ertan arch dam, the proposed model outperformed traditional methods in prediction accuracy and long-term stability. Achieving the lowest average root mean square error (2.677 mm) and average mean absolute error (2.325 mm), the LAC-MOGP model demonstrates significant effectiveness and practical applicability for reservoir slope deformation forecasting.