In this work, TM-N4 sites embedded on curved C70 Fullerenes (TMN4(n)@C64, n = 1–5 which denote the doping positions and TM = Sc-Zn) were designed as efficient catalysts for electrochemical CO2 reduction reaction (ECO2RR). With interpretable machine learning (ML) and first-principles calculations, the performance of TM-N4 sites as possible electrocatalysts for ECO2RR under the curvature effect were revealed. After the screen procedure of catalyst stability, CO2 activation and selectivity of 50 designed systems, VN4(3), CrN4(3), MnN4(2), FeN4(2, 3, 5)@C64 were identified as high performance catalysts for ECO2RR to CH4, with limiting potential of -0.41 V, -0.26 V, -0.28 V, -0.35 V, -0.25 V and -0.17 V respectively, outperform the TM-N4 catalysts on the flat surface. A key structural descriptor K which can reflect the curvature effect of TM-N4 sites on the curved surface was identified with ML models. The gradient boosting regression (GBR) and sure independence screening and sparsifying operator (SISSO) algorithm reveal that the structural parameter K have strong association with the CO2 adsorption and significant influence on the selectivity of catalysts. Interestingly, the structural parameter K can influence more the binding of O-bonded intermediates than C-bonded intermediates. With the GBR algorithm, an effective model for the prediction of the limiting potential for C1 products were obtained. This study clarified the curvature effect of TM-N4 supported on the curved surface, and provides valuable guidance for the designing of TM-N4 single atom catalysts for ECO2RR on curved surface.