In the problem of the protection by the consequences of an explosion is actual for many industrial application involving storage of gas like methane or hydrogen, refuelling stations and so on. A simple and economic way to reduce the peak pressure associated to a deflagration is to supply to the confined environment an opportune surface substantially less resistant then the protected structure, typically in stoichiometric conditions, the peak pressure reduction is around the 8 bars for a generic hydrocarbon combustion in an adiabatic system lacking of whichever mitigation system. In general the problem is the forecast of the peak pressure value (PMAX) of the explosion. This problem is faced using CFD codes modelling the structure in which the explosion is located and setting the main parameters like concentration of the gas in the mixture, the volume available, the size of vent area and obstacles (if included) and so on. In this work the idea is to start from empirical data to train a Neural Network (NN) in order to find the correlation among the parameters regulating the phenomenon. Associated to this prediction a fuzzy model will provide to quantify the uncertainty of the predicted value.