Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanism are not well understood. To resolve this issue, a new method for the inverse analysis of turbidity current using deep learning neural network (DNN) was proposed. This research aims at verifying and calibrating this method using artificial and flume experiment datasets. The forward model based on the shallow water equation was employed in this study to produce artificial datasets of turbidite deposits. DNN was applied to two hundred artificial datasets and two sets of experiment data. As a result of inversion by DNN, spatial distributions of grain size and thickness of experimental turbidites were reconstructed accurately. With regard to hydraulic conditions, the flow heights were reasonably estimated, and sediment concentrations reconstructed were reasonable except for regions where the values measured in experiments were extremely low. Flow duration also showed reasonable reconstructed values. In contrast to the other values, there was a large discrepancy between the measured and reconstructed values of flow velocity. The reason for this discrepancy in velocity may be attributed to inaccuracy in closure functions employed in the forward model. Future application to actual data of natural scale turbidite is anticipated.