Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The R 2 {R^{2}} reached by the model was 94 %, and MAE was 12.40 grams/m2. Using the same dataset, the fine tree regression model showed an R 2 {R^{2}} of 99 % and an MAE of 3.35 grams/m2. Additionally, a dataset not included in the building process was used to validate the method’s performance. The results showed that ANN-based modeling has a higher predictive capability than the regression tree model. Therefore, this model was embedded in a graphic user interface that was developed in Python.