Machine learning (ML) is a promising method for finding optimal materials for the synthesis of effective catalysts. However, the application of ML to the exploration of effective heterogeneous thermal catalysts, such as those for dry reforming of methane (DRM), is challenging because the performance of such catalysts is based on various complex factors. To address this issue, we propose an interpretable machine learning (IML) framework to predict the performance of a DRM catalyst. CH 4 conversion, a performance indicator of DRM catalysts, is a function of design variables (such as composition and components of active metal, support, and promoter), pretreatment conditions (such as calcination and reduction), and operating conditions. The framework predicts catalyst performance while considering the contribution of reaction temperature, the most dominant variable in DRM reaction, by using an ML model. First, the shapley additive explanation (SHAP) and partial dependence plot (PDP) are systematically used as an IML tool in a framework for developing reliable and transparent ML models. The prediction accuracy of the model improved (R 2 : 0.91 to 0.96) after data set modification using SHAP. Second, PDP was used to obtain optimal operational and pretreatment variables. A dataset was generated via PDP to reflect operation and pretreatment variables while reducing the contribution of the reaction temperature to CH 4 conversion predictions. The contribution of each variable to the conversion was described plausibly, and promising DRM catalyst candidates at each reaction temperature (600, 700, and 800 °C) were recommended using the developed framework.