Methane hydrate reservoirs are a potential alternative energy source identified in the recent years. This work develops a methodology to predict the gas production potential and hydrological transport properties of methane hydrate reservoirs using artificial neural network coupled with genetic algorithm. Hydrate dissociation due to depressurisation in a lab scale core recovered from field excavation is modelled as multi-component, multi-phase, chemically reacting, non-isothermal flow in porous medium with effective transport properties. A predictive surrogate model, mimicking the full scale model along with reservoir heterogeneity is then developed using a feed forward back-propagation artificial neural network trained using the Levenberg-Marquardt algorithm coupled with Bayesian regularization. Predictions of gas production potential obtained from the surrogate model are then compared with those obtained from the full numerical simulations as well as measurements reported in literature for prescribed values of hydrological transport properties. It is then interfaced with genetic algorithm to solve an inverse estimation problem involving prediction of reservoir hydrological properties namely porosity, intrinsic permeability and initial hydrate saturation from temporally varying gas production. The effect of uncertainties associated with measurement of gas production on the predicted hydrological properties is investigated numerically and suitable bounds are proposed based on a statistical analysis. © 2019 Elsevier Ltd