One of the most crucial aspects of quality assurance is checking products for faults before they are sold or distributed. For a customer to enjoy a product, a high-quality product is more crucial than having more of the same. An important factor in determining how good a product is, is the consumer. A different perspective on quality is as the whole of the factors that go into creating goods that consumers like. Recently, there has been a significant increase in the use of machine learning and image processing to improve the surface quality of fruits and other commodities. Its is mainly because these technologies greatly outperform what the human eye can do. This suggests that by using computer vision and methods for image processing can shorten the time-consuming and subjective industrial quality control processes. This article discusses using picture segmentation and machine learning to check and assess food. It is capable of both fruit classification and rottenness detection. Gaussian elimination is first used to reduce noise from photos. The quality of the photos is then improved using histogram equalization. The segmentation of the image is done using the K-means clustering technique. After that, machine learning methods like KNN, SVM, and C4.5 are used to classify fruit & Food photos. These algorithms determine if a fruit has been injured or not.