Aiming at the complexity and spatial variability of the dynamic soil water and salinity in the saline region of the Lower Yellow River Delta, artificial neural network was introduced for modeling and prediction of soil water and salinity. Influence of the number of neurons in the hidden layer on training and forecasting was discussed for the three-layered network, and Back Propagation Neural Network (BPNN) models were established for modeling contents of water and salinity and their spatial distribution in the surface soil 0~20 cm in depth. Results indicate that the water and salinity in the surface soil was significantly correlated with soil bulk density and groundwater properties across the study area. For surface soil salinity, it is advisable to have the five variables, i.e. longitude and latitude of the site, soil bulk density, and depth and mineralization of groundwater cited as input vectors, while for soil moisture, the four variables, i.e. longitude, latitude, bulk density, and groundwater depth. An excessive number of neurons in the hidden layer would result in overfitting. Considering forecasting precision, the topological structure of the BP network was defined as 5∶8∶1 and 4∶6∶1 for salinity and moisture in the surface soil, respectively. Distribution maps of the observed surface soil water and salinity and their BPNN simulation displayed similarity in spatial pattern, and the BPNN effectively simulated contents of water and salinity and their spatial distribution in the surface soil with high accuracy. The findings of the study can serve as a theoretical basis for analyzing the occurrence, development and evolvement regularities of soil salinization in the Yellow River Delta, and provide a scientific basis for decision-making in regulating soil water and salt regulation and implementing scientific management of saline soils.