The increasing need for energy has led scientists and engineers to explore renewable sources, and biodieselBiodiesel has emerged as a promising alternative that is also environmentally friendly. This chapter will look at how machine learning, a type of artificial intelligence, can help us predict how much biodiesel we can get from a process called transesterificationTransesterification, which involves different catalysts. These catalysts can be homogeneous, heterogeneous, or enzyme-based. Of the methods tested, a technique called extreme gradient boosting gave the most accurate predictions. It was able to predict biodiesel yields with a high degree of accuracy, as shown by a determination coefficient of almost 0.98, after being tested rigorously through ten-fold cross-validation. The analysis also showed which factors are most important for getting good biodiesel yields. For homogeneous catalysts, linoleic acid levels were critical. For heterogeneous catalysts, it was behenic acid. And for enzyme catalysts, the reaction time was the most important factor. These findings help us understand how these factors, both individually and in combination, affect how well different catalysts work. This understanding is valuable for making biodiesel production more efficient.