We constructed an ML-driven GA to tackle the inverse design problem of polymer membranes for CO 2 separation. We showed that the hashed-based ECFP4 yields lower predictive errors on the test sets than the substructure keys-based fingerprints. We presented different regressionbased ML models, where random forest regression models resulted in the lowest RMSE and highest R 2 values for both the test and the training set. Although random forest regression models can successfully predict both the gas permeability and the selectivity, we used models trained on the gas permeability, since these models have better predictive power than the models trained on the selectivity. After obtaining the ML models to predict the performance of any polymer membrane candidate, we implemented a data-driven inverse design algorithm to efficiently explore the polymer material space. In theory, one can use any inverse design algorithm for such a problem, but we have implemented a GA since it has been successfully used for other polymer applications. We created the gene pool using the BRICS algorithm on the experimental data and obtained 79 unique genes to initiate the GA process with 100 parent polymers that have 4 genes in their monomer unit. Fitness function is a key driving parameter in the GA, and as such, we used