The gas Centrifuge is a very hard equipment to model, because it involves a gas dynamic with many complications, such as hypersonic waves and rarefied regions combined with continuous flow areas. Therefore, data analysis regressions remain currently a very important technique to understand and describe the problem in a practical way. This paper intends to apply and compare several regression techniques using machine learning, to obtain a hydraulic and a separative power model of gas centrifuge used in enrichment plants. For this purpose, a set of normalized data composed of 134 experimental lines was used, observing the variables of interest, the separation power (dU), and the waste pressure (Pw), through the following explanatory variables: feed flow (F), cut (q), and product pressure (Pp). The comparisons were presented between the results obtained for the models generated by the following: algorithms, multivariate regression, multivariate adaptive regression splines ??? MARS, bootstrap aggregating multivariate adaptive regression splines ??? Bagging MARS, artificial neural network ??? ANN, extreme gradient boosting ??? XGBoost, support vector regression??? Poly SVR, radial basis Function support vector regression ??? RBF SVR, K-nearest neighbors ??? KNN and Stacked Ensemble. That way, to avoid overfitting and provide insights about generalization of the models in unseen data, during the training phase, the k-fold cross validation approach was used. Subsequently, the residuals were analyzed, and the models were compared by the following metrics: Root mean square error ??? RMSE; Mean squared error ??? MSE; Mean absolute error ??? MAE; and Coefficient of determination ??? R2.