The quality assessment of vegetables is essential for ensuring consumer satisfaction and maintaining food safety standards. In this study, we propose an application that utilizes machine learning algorithms to predict the quality of vegetables based on various attributes. The application employs a dataset comprising features such as color, texture, size, and firmness, which are commonly associated with vegetable quality. We explore the effectiveness of several machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, in predicting vegetable quality. The dataset is preprocessed to handle missing values, normalize features, and address class imbalance issues. Through extensive experimentation and cross-validation, we identify the most suitable machine learning model for accurate vegetable quality prediction. The developed application provides a user-friendly interface for farmers, distributors, and consumers to assess the quality of vegetables quickly and reliably. This research contributes to improving the efficiency of vegetable supply chains and enhancing consumer confidence in the produce they purchase.