The transient, cyclic nature and the flexibility in process design makes the optimization of pressure-swing adsorption (PSA) computationally intensive. Two hybrid approaches incorporating machine learning methods into the optimization routines are described. The first optimization approach uses artificial neural networks as surrogate models for function evaluations. The surrogates are constructed in the course of the initial optimization and utilized for function evaluations in subsequent optimization. In the second optimization approach, important design variables are identified to reduce the high dimensional search space to a lower dimension based on partial least squares regression. The accuracy, robustness and reliability of these approaches are demonstrated by considering a complex 8-step PSA process for pre-combustion CO2 capture as a case study. The machine learning-based optimization ∼10x reduction in computational efforts while achieving the same performance as that of the detailed models.