Pressure and rate transient analyses have been widely used to determine the properties of a reservoir, such as boundary types, permeability, and formation pressure. Typically, one analytical model must be selected first to interpret the transient data; however, it is very difficult to determine which model suits the reservoir conditions. In this study, a hybrid deep learning (DL) model is developed to offer an alternative approach to characterize the reservoir (i.e., reservoir shape, flow boundary type, and porosity system) using pressure and rate transient data interpretation in unconventional tight and shale reservoirs. A multivariate synthetic data set of pressure and rate transients is first generated from the analytical solutions. Five DL architectures of recurrent, convolutional, and hybrid recurrent-convolutional types are trained on a multivariate time series of five features: pressure, pressure derivative, normalized rate, normalized cumulative production, and normalized integral derivative cumulative production. The performances of these architectures are compared, and the hybrid convolutional neural networks–long short-term memory leads to the highest performance with an accuracy of 0.98 on validation data. The performance of the optimum architecture is examined on transient data generated from reservoir simulations, and results demonstrate that the developed architecture can correctly characterize the reservoir with a accuracy of 98%. The applicability of the hybrid architecture on different length and resolution time series is also tested. It is found that the short-time flow regimes of wellbore storage and initial transition do not affect the machine learning architecture’s classification performance, and the boundary-dominated flow is the main regime for classification of these types of resources.