Pepper is a valuable medicinal substance and an expensive aromatic. For profit purposes, some vendors adulterate dried papaya seeds with black pepper due to their physical similarities. This impurity can lead to various health issues. Several existing methods are available to detect this adulteration, but they have some limitations. To overcome these challenges, the study employed a technique called Hyperspectral Imaging (HSI) by using machine learning classification algorithms. This research experimented with various machine learning classification algorithms, including Decision Tree, Random Forest, and Linear Discriminant Analysis (LDA). Among these algorithms, the Decision Tree algorithm stood out as the most effective in achieving an impressive classification accuracy of 99.93%, with a computational time of 6.76 seconds. This hyperspectral imaging analysis and the machine learning classification hold significant promise in enhancing food quality assurance, ensuring consumer health, and reinforcing trust within the industry.