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 novel method for the inverse analysis of turbidity current using deep learning neural network (DNN) was proposed. This study aims to verify this method using artificial and flume experiment datasets. Development of inverse model by DNN involves two steps. First, artificial datasets of turbidites are produced using a forward model based on shallow water equation. To develop a inverse model, DNN then explores the functional relationship between initial flow conditions and characteristics of the turbidite deposit through the processing of artificial datasets. The developed inverse model was applied to 200 sets of artificial test data and four sets of experiment data. Results of inverse analysis of artificial test data indicated that the flow conditions can be precisely reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were accurately reconstructed. With regard to hydraulic conditions, reconstructed values of flow heights, sediment concentrations, and flow durations were close to the measured values. In contrast to the other values, there was a larger discrepancy between the measured and reconstructed values of flow velocity, which may be attributed to inaccuracies in sediment entrainment functions employed in the forward model.