Single-atom catalysts (SACs) with N-heterocyclic carbene (NHC) coordination provide an effective strategy for enhancing nitrogen reduction reaction (NRR) performance by modulating the electronic properties of the metal active sites. In this work, we designed a novel NHC-coordinated SAC by embedding transition metals (TM) into a two-dimensional C2N-based nanomaterial (TM@C2N-NCM) and evaluated the NRR catalytic performance using a combination of density functional theory and machine learning. A multi-step screening identified eight high-performance catalysts (TM = Nb, Fe, Mn, W, V, Ta, Zr, Ti), with Nb@C2N-NCM showing the best performance (limiting potential = -0.29 V). All catalysts demonstrated lower limiting potential values compared to their TM@graphene-NCM counterparts, revealing the effectiveness of the C2N substrate in enhancing catalytic activity. Machine learning analysis achieved high predictive accuracy (coefficient of determination = 0.91; mean absolute error = 0.19) and identified final step protonation (S6), Mendeleev number (Nm), and d-electron count (Nd) as key factors influencing catalytic performance. This study offers valuable insights into the rational design of NHC-coordinated SACs and highlights the potential of C2N-based nanomaterials for advancing high-performance NRR electrocatalysts.