Machine　learning　(ML)　has　addressed　the　traditional　challenges　of　large　data　processing　in　density　functional　theory　(DFT)　calculations.　However,　understanding　the　relationship　between　fundamental　descriptors　and　catalytic　performance　and　identifying　key　drivers　of　catalytic　activity　remain　challenging.　Here,　we　present　a　cost-effective,　high-throughput,　and　interpretable　ML　method　to　accurately　identify　nitrogen　reduction　reaction　(NRR)　performance　determinants.　Initially,　378　M1M2@TiO2　catalysts　are　screened,　yielding　33　promising　candidates　through　high-throughput　techniques.　Subsequently,　ML　models　(primarily　XGBoost)　predict　free　energy　changes　of　key　NRR　intermediates.　Shapley　Additive　Explanations　(SHAP)　analysis　identifies　two　critical　features:　the　M-1-N-N　bond　angle　(M1NN)　and　the　M-2-N　bond　length.　Four　catalysts　exhibiting　energy　changes　below　0.3　eV　in　the　potential-determining　step　are　identified　as　promising　candidates.　Combined　SHAP　analysis　and　electronic　structure　calculations　confirm　the　inherent　activity　of　NRR　catalysts,　highlighting　the　importance　of　fundamental　properties　in　modulating　active　sites　for　superior　NRR　performance.