Using the trained ML models, we perform high-throughput screening of over nine million hypothetical polymers with unknown permeabilities, including many polyimides and ladder polymers that can be classified as PIMs. Thus, we identify thousands of promising polymers for gas separation membranes with desirable performance, which lie well above 2008 Robeson upper bounds. Finally, we perform high-fidelity MD simulations to confirm that the ML-predicted permeabilities of top-performing polymers are very accurate. Overall, our ML-assisted workflow is a promising method for the discovery of innovative polymers for next-generation gas-separation membranes to advance energy and environmental sustainability.