Deepwater exploration faced prominent drilling safety risks. Offset well data was scarce at the exploration stage. This study proposed a 3D geomechanical modeling method. This method integrated seismic interval velocity and machine learning. Taking Oilfield A in the South China Sea as the study area, a prediction model based on the LSTM-BP fusion neural network was constructed to correct seismic interval velocity and invert the three-dimensional shear wave velocity and rock density of the study area. The elastic modulus, Poisson"s ratio, cohesion, and internal friction angle were calculated using empirical formulas. A three-dimensional pore pressure model was established using Eaton"s method, and the three-dimensional in-situ stress field was solved by combining the poro-elastoplastic constitutive model with the finite element method, and a three-dimensional safe drilling fluid density window was constructed ultimately. The results indicated that the prediction accuracy of the LSTM-BP fusion model was better than that of the single LSTM and BP models, with R2 values of the seismic interval velocity correction model, shear wave velocity prediction model, and rock density prediction model reaching 0.9406, 0.957, and 0.8771, respectively. The established 3D model could accurately and finely characterize the vertical and lateral heterogeneity of formations, and identified abnormal high pressure and extremely narrow safe density windows in the lower Yinggehai Formation and Huangliu Formation (minimum window was only 0.1 g/cm3), providing technical support for intelligent drilling and completion in deepwater.