The quality assessment of food products is a crucial aspect of the food industry, ensuring both safety and consumer satisfaction. Traditional methods of assessment are often subjective and time-consuming. This research explores the application of the K-Nearest Neighbors (K-NN) algorithm as a data-driven approach to food quality assessment. Leveraging a dataset encompassing various quality attributes, including color, texture, aroma, and taste, we demonstrate that K-NN offers an objective and efficient means of classifying food products. Through rigorous evaluation and comparisons with traditional methods, this study underscores the potential of K-NN in enhancing food quality assessment procedures