To tune single-atom catalysts (SACs) for effective nitrogen reduction reaction (NRR), we investigate various transition metals implanted on boron-arsenide (BAs), boron-phosphide (BP), and boron-antimony (BSb) using density functional theory (DFT). Interestingly, W-BAs shows high catalytic activity and excellent selectivity with an insignificant barrier of only 0.05 eV along the distal pathway and a surmountable kinetic barrier of 0.34 eV. The W-BSb and Mo-BSb exhibit high performances with limiting potentials of -0.19 and -0.34 V. The Bader-charge descriptor reveals that the charge transfers from substrate to *NNH in the first protonation step and from *NH3 to substrate in the last protonation step, circumventing a big hurdle in NRR by achieving negative free energy change of *NH2 to *NH3. Furthermore, machine learning (ML) descriptors are introduced to reduce computational cost. Our rational design meets the three critical prerequisites of chemisorbing N2 molecules, stabilizing *NNH, and destabilizing *NH2 adsorbates for high-efficiency NRR.