The traditional regression model usually simulates the influence of water pressure and rainfall in the early stage based on experience, but it is not suitable. To solve this problem, the normal distribution curve is used to simulate the lagging effect of water pressure and rainfall on dam seepage. In view of problem of slab cracks, the influence of cracks on seepage is analyzed. In this paper, a safety monitoring model for concrete face rockfill dam (CFRD) seepage with cracks considering the lagging effect is proposed, in which slab cracks are considered as an influencing factor. The radial basis function neural network (RBFNN) optimized by genetic algorithm (GA) is used to establish a safety monitoring model for a CFRD seepage. Seepage of the dam is predicted by this model, whose results are similar to the monitoring data, which indicates that the method has certain applicability. Through the analysis of the proportion of factors affecting CFRD seepage, it is found that the rainfall component has the greatest impact on the total seepage, accounting for more than 50%, and the crack component accounts for about 10%. Finally, through the cloud model, the monitoring index of CFRD seepage is worked out, which has certain guiding significance for the treatment of abnormal seepage monitoring data.