This study proposes a machine learning approach to understand how post-earnings-announcement drift (PEAD) works. We analyze when PEAD, combined with other factors, becomes more pronounced. To accommodate diverse variables and more complex specifications, two tree-based machine learning approaches including eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) are used to examine the relationship between PEAD and 89 variables. The long-short portfolio produced by LightGBM model reports 2.1 times higher returns than the portfolio's returns, based on the conventional measure of earnings surprise. The model enhances the economic and statistical significance of the long-short portfolio returns. SHapley Additive exPlanations (SHAP) analysis determines feature importance and shows that liquidity, firm size, profitability ratios, share turnover, net trading flows by retail investors, and earnings surprises, play an important role in the prediction of PEAD.