In response to the increased demand for sustainable energy alternatives, biodiesel production and use are being studied. Several process factors influence biodiesel production, which must be maintained at optimal levels for efficiency. Practical research are needed before making any conclusions about transesterification raw materials, which can affect biodiesel output and quality. Due to the large number of process variables and their non-linear feedback connection, aggressive techniques cannot attain optimal process parameters. This research uses a machine learning-based prediction approach to quantify biodiesel production's response to process parameters. This research analyses four effective machine learning algorithms for biodiesel yield prediction: linear regression, random forest regression, adaBoost regression, and artificial neural network. The random forest regression and adaBoost regression algorithms accurately predicted biodiesel yield in modelling. Due to its lowest inaccuracy, random forest is the best algorithm for simulating transesterification-based biodiesel synthesis. Due to its insensitivity to regressor quantity, random forest algorithm has faster deployment times.