Coriander is a widely used spice, valued for its flavor, aroma, and nutritional benefits in various cuisines and food products. However, adulteration, such as the addition of sawdust, poses significant risks to food safety and authenticity. This study aims to present a solution for predicting sawdust adulteration in coriander powder by providing a detailed methodology for utilizing machine learning-assisted FTIR spectroscopy. It employs various base models, including linear regression (LR), decision tree (DT), support vector regression (SVR), and artificial neural network, (ANN), for adulteration detection. It was observed that the original dataset and Savitzky-Golay smoothed dataset (dataset generated after preprocessing) yielded superior results by achieving R