Tsunami deposits provide information for estimating the magnitude and flow conditions of paleo-tsunamis, and the inverse model has potential for predicting hydraulic conditions of tsunamis from their deposits. Majority of the previously proposed models are based on oversimplified assumptions and share limitations in applicability to original tsunami hydrodynamics in transportation and deposition settings. We present a new inverse model that serves as a modified version of the previously proposed model FITTNUSS, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses the deep neural network (DNN) for the inversion method. In this method, forward model calculation was repeated at random initial flow conditions to produce artificial training data sets that represent depositional characteristics such as thickness and grain size distribution. Subsequently, the DNN was trained for establishing the general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that the established DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku-Oki tsunami deposits. Jackknife resampling was applied for estimating the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4±0.140 m/s and 4.11±0.152 m, respectively after the uncertainty analysis. The DNN showed promising results for reconstruction of the event from natural data set, which would help in estimating the hydraulic conditions of paleo-tsunamis based on realistic settings of tsunami deposits.