Biodiesel has evolved as a renewable and environmentally friendly energy source that has the potential to reduce global warming. As a result, this work investigates data-operated machine learning strategy for biodiesel yield estimation via regression. Using Box Behnken design, the researchers looked at time (4–8 minutes), methanol/oil mole ratio (30–50%), volume (100–300 mL), and catalyst concentration (1–2 wt%). Statistical performance gauge showed SVM (Root mean square error (RMSE) = 1.20, R2 =0.91 and Mean square error (MSE) = 1.45) and ANN (R2 = 0.86, RMSE = 1.55, MSE = 2.42) models narrate process with excellent precision differentiate to RSM (R2 = 0.85, RMSE = 1.67, MSE = 2.78). Properties are measured as per standard.