Cuprates, a member of high-Tc superconductors, have been on the long-debate on their superconducting mechanism, so that predicting the critical temperature of cuprates still remains elusive. Herein, using machine learning and first principle calculations, we predict the maximum superconducting transition temperature (Tc,max) of hole-doped cuprates and suggest the explicit functional form for Tc,max with the root-mean-square-error of 3.705 K and the coefficient of determination R 2 of 0.969. We employed two machine learning models; one is a parametric brute force searching method and another is a non-parametric random forest regression model. We have found that material dependent parameters such as the Bader charge of apical oxygen, the bond strength between apical atoms, and the number of superconducting layers are important features to estimate Tc,max. Furthermore, we predict the Tc,max of hypothetical cuprates generated by replacing apical cations with other elements. When Ga is an apical cation, the predicted Tc,max is the highest among the hypothetical structures with 71, 117, and 131 K for one, two, and three CuO2 layers, respectively. These findings suggest that machine learning could guide the design of new high-Tc superconductors in the future.Understanding the material dependence of superconducting temperature (Tc) has been a longstanding subject of importance in the condensed matter physics community. However, this becomes especially challenging for the high Tc cuprates, as their underlying mechanism of superconductivity, despite intensive experimental and theoretical studies, still remains elusive over 30 years since the discovery of La2CuO4 [1]. All cuprate superconductors share the common characteristics of the two-dimensional CuO2 superconducting layer and strong electronic

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