Ghee, a popular clarified butter widely consumed around the world, particularly in India, is valued for its taste and health benefits. However, some vendors adulterate it with cheaper substances such as vanaspati to increase profits, which can be harmful to consumers. This requires robust methods for quality assurance. In response to this challenge, this article presents a noninvasive method for detecting ghee adulteration with vanaspati using hyperspectral imaging (HSI). We created a data set consisting of hyperspectral images with different proportions of ghee and vanaspati. This data set was tested on various machine-learning algorithms. The results were impressive, showing a highly accurate detection of adulteration (99. 35%) with the K-Nearest Neighbor (KNN) and Random Forest algorithms. These methods were quick to converge, facilitating faster results.