Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanisms are not well understood. This study proposes a novel method for the inverse analysis of turbidity currents using a deep learning neural network (DNN) to better explore the properties of turbidity currents. The aim of this study is to verify the DNN inverse method using numerical and flume experiment datasets. Numerical datasets of turbidites were generated with a forward model. Then, the DNN model was trained to find the functional relationship between flow conditions and turbidites by processing the numerical datasets. The performance of the trained DNN model was evaluated with 2000 numerical test datasets and 5 experiment datasets. Inverse analysis results on numerical test datasets indicated that flow conditions can be reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were consistent with the sample values. Concerning hydraulic conditions, flow depth H, layer-averaged velocity U, and flow duration Td were reconstructed with a certain level of deviation. Greater discrepancies between the measured and reconstructed values of flow concentration were observed relative to the former three parameters (H, U, Td), which may be attributed to difficulties in measuring the flow concentration during experiments. The precision of the reconstructions for experimental datasets was estimated using Jackknife resampling. Although the DNN model did not provide perfect reconstruction, it proved to be a significant advance for the inverse analysis of turbidity currents.