This paper explores the use of hyperspectral imaging (HSI) and machine learning (ML) techniques to detect and classify oil adulteration. The study aims to address the global concern of food adulteration, which poses significant health risks and erodes consumer trust. Using a non-destructive HSI system, the researchers analyzed 670 oil samples, including pure and adulterated oils, to develop an accurate and efficient fraud detection method. The Savitzky-Golay filter was applied to preprocessed images to remove noise and smooth spectral signatures. Various ML algorithms, such as Support Vector Machines (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forests (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), and Naïve Bayes, were used for oil identification. LDA was found to be the most effective model, achieving a validation accuracy of 100%. The proposed method offers a robust pipeline for effective oil adulteration detection, enhancing food safety and quality control. The study contributes to the development of intelligent systems for automatic oil adulteration detection, providing a comprehensive solution to the complex challenge of oil adulteration.This paper explores the use of hyperspectral imaging (HSI) and machine learning (ML) techniques to detect and classify oil adulteration. The study aims to address the global concern of food adulteration, which poses significant health risks and erodes consumer trust. Using a non-destructive HSI system, the researchers analyzed 670 oil samples, including pure and adulterated oils, to develop an accurate and efficient fraud detection method. The Savitzky-Golay filter was applied to preprocessed images to remove noise and smooth spectral signatures. Various ML algorithms, such as Support Vector Machines (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forests (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), and Naïve Bayes, were used for oil identification. LDA was found to be the most effective model, achieving a validation accuracy of 100%. The proposed method offers a robust pipeline for effective oil adulteration detection, enhancing food safety and quality control. The study contributes to the development of intelligent systems for automatic oil adulteration detection, providing a comprehensive solution to the complex challenge of oil adulteration.