In this thesis we consider the problem of the uncertainty on the GeneralizedSignal to Noise Ratio (GSNR) value over fiber optics. We work in an opticalnetwork of Optical Line Systems (OLSs) where all the capacity comes from thetransmission layer. Our project is based on a Python implementation. We studythe GSNR distribution in realistic scenario applying Monte Carlo and machinelearning (ML) techniques.We started by considering a trivial example in which we consider a periodic OLS.Then, we generalize the problem by randomly changing the physical parametersregarding fibers and EDFAs(length, dispersion, connector losses, gain ripples) inorder to reproduce realistic uncertainties over these parameters.We study the variation of the GSNR distribution due to uncertainties over theconsidered parameters assuming Gaussian fluctuations around reference values.We create a dataset of GSNR variations using a python abstraction of the OLSsthat simulates the signal propagation with different parameter values.