3D NAND flash memory faces unprecedented complicated interference than planar NAND flash memory, resulting in more concern regarding reliability and performance. Stronger error correction code (ECC) and adaptive reading strategies are proposed to improve the reliability and performance taking a threshold voltage (Vth) distribution model as the backbone. However, the existing modeling methods are challenged to develop such a Vth distribution model for 3D NAND flash memory. To facilitate it, in this paper, we propose a machine learning-based modeling method. It employs a neural network taking advantage of the existing modeling methods and fully considers multiple interferences and variations in 3D NAND flash memory. Compared with state-of-the-art models, evaluations demonstrate it is more accurate and efficient for predicting Vth distribution.