Only a limited number of predictors can be included in a generalized linear mixed model (GLMM) due to estimation algorithm divergence. This study aims to propose a machine learning based algorithm (e.g., random forest) that can consider all predictors without the convergence issue and automatically searches for the optimal GLMMs.