The biodiesel is an attractive option to substitute higher higher-emission diesel. However, the transesterification process used for biodiesel making is complex and nonlinear. Hence, optimization becomes important for improving yield and reducing the costly resources. One-Factor-at-a-Time (OFAT) and Response Surface Methodology (RSM) are two common ways to improve biodiesel production; however, they typically have trouble with complicated nonlinear relationships and don’t work well with new data. As the manufacture of biodiesel from used frying oil on a big scale picks up speed, there is an urgent need for modelling methods that are more precise, understandable. This study uses an Explainable Machine Learning (EML) framework that combines predictive algorithms and SHapley Additive exPlanations (SHAP) to model and interpret the transesterification process and its predictive modeling. In this study, three supervised ML models: LASSO regression, Decision Tree (DT), and Extreme Gradient Boosting (XGBoost) were developed. XGBoost stood out from the others with almost flawless training accuracy (R2 = 0.9999) and strong generalization (test R2 = 0.7850). The SHAP analysis was used to interpret the interpretation of XGBoost-based model. The most important factor was reaction temperature. The SHAP bee-swarm and comparison plots showed both the relevance of global features and the influence of particular features, making the model clearer. This EML framework not only makes predictions more accurate, but it also offers useful information on how to optimize your processes. This makes it possible to produce biodiesel from waste feedstocks in a more efficient, reliable, and scalable way.