The present study aims to model and simulate the behavior of a flat glass annealing lehr, by means of data analysis algorithms. In the framework of “Industry 4.0”, the availability of more production data and the need to find correlations between process variables, enhance the use of Machine Learning to generate models that are able to predict a process or equipment outputs, given certain inputs. The target of this work is to create a regression model that predict the permanent stress of a glass sheet (output) generated in the annealing phase of the controlled cooling. The inputs considered are the setting of the machine and other working variables such as production parameters, initial and boundary conditions.