To unlock natural gas from shale, placing horizontal wells equipped with multi-stage hydraulic fracturing is crucial. Due to complexities and uncertainties of formation characteristics and stimulation design parameters, field optimization studies can be challenging. In addition, several models for hydraulic fracturing have been proposed. However, equivalency criteria between models have not yet been fully understood. In this paper, we propose a new approach to tackle some of these challenges through the coupling of type curves and artificial neural network based models. The complete framework and results of type curves and ANN-assisted prediction of equivalent systems are shown and discussed. The first part of the work will focus on ANNs for establishing equivalencies between different hydraulic fracture representations. ANNs which are powerful tools capable of establishing relationships between highly non-linear parameters have been developed to assist numerical reservoir simulations in predicting production profiles and establishing equivalencies between two hydraulic fracture representations for a wide range of parameters describing various systems. The equivalent systems yield overall production profiles and cumulative productions within ±15% discrepancy between the two hydraulic fracture representations at the end of the simulation. In the second part of this work, analysis of pressure transient drawdown data through the developed type curves coupled to ANNs is performed. The pressure transient analysis enables us to obtain key properties of the stimulated zone and identify the more probable equivalent systems. The crushed zone properties are then used in the developed ANNs. The ANNs can instantly predict gas production rates as well as establish its equivalent transverse hydraulic fracture representations. Another ANN is then used to predict gas production rates based on the predicted transverse fracture parameters. The results show that gas production rates and cumulative production from ANNs and numerical model based on parameters predicted by type curves are in good agreement with the base case. This proposed unique approach through the coupling of type curves and ANNs can assist in analysis and reservoir simulation studies for optimization of shale gas development projects in a rapid manner.