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