Recent advancements in gas-sensitive materials based on metal oxides have mainly relied on experimental trial and error, which is time-consuming and costly. To address this, a novel approach combining first-principles calculations and machine learning is proposed to predict the gas response properties of materials. Copper oxide (CuO) is used as a representative material for validation. Six characteristic parameters are selected at the electron and atomic structure level, including adsorption energy (Eads), bandgap (Eg), distortion degree, conduction band minimum (CBM), valence band maximum (VBM), and bond length (d), to build an accelerated gas response discovery model. The results indicate that gas response is determined by changes in these parameters upon gas adsorption, though no direct correlation is found. Machine learning algorithms are applied to establish correlation models, achieving an accuracy of 83.75%. Analysis reveals that the distortion degree has the most significant impact on a gas response (28.57%), while the VBM contributes the least (4.76%). CuO exhibits a strong response to gases like CHO, CHO, CO, H, and NO, but minimal response to CH1N and CH, consistent with literature findings. This work offers new insights for sensor development and could enhance the efficiency of material discovery in gas sensing applications.