Our study demonstrates the powerful synergy between ML and HT automation in uncovering new polar cocrystals, specifically those based on CA. We identified the HOMO-LUMO energy gap as a critical, yet previously underexplored, factor influencing the formation of polar cocrystals, leveraging interpretable ML techniques and Shapley Additive Explanations (SHAP). Along with fingerprint similarity and co-crystallization prediction, these insights drove efficient candidate selection from vast chemical spaces. Experimentally, we validated our predictions by discovering 16 previously unreported hydrogen-bonded cocrystals in a 1:1 stoichiometric ratio, 8 of which crystallized in polar space groups. The proportion of polar cocrystals observed in this study, achieving a 50% success rate, significantly exceeds the ~14% statistical frequency reported in the CSD. This result underscores the efficacy of our data-driven approach in promoting the discovery of polar materials.