While artificial neural networks (ANNs) have been used to forecast groundwater levels at single wells, they have not been constructed to forecast hydraulic heads in both space and time. This seminal study presents a modeling framework, guided by the governing physical laws, for building an integrated space–time ANN (IST–ANN) model for regional groundwater level predictions. IST–ANN shows promise for parsimoniously modeling regional-scale groundwater levels using available surrogate information.