This paper investigates multiple methods of machine learning for an application of surrogate modeling and optimization of an experimentally validated pressure swing adsorption model, evaluating their accuracy and precision compared to the more widely used method of artificial neural networks. In addition, some means of improving the machine learning model’s accuracy with at-hand process knowledge and parameters were explored, which was followed by the optimization of the purity and recovery parameters of the system, finishing with a quantification of the total computational time employed. All steps described were developed and finished successfully using the open-source Python programming language, and the expected and unexpected results were discussed.