The project introduces an innovative and technology-driven solution to the agricultural sector by harnessing the power of image processing and machine learning. Mangoes, a popular tropical fruit, are renowned for their diversity in quality and ripeness levels. Ensuring consistent fruit quality is crucial for both producers and consumers. This project aims to address this challenge by developing a system that can predict mango quality attributes, such as ripeness and freshness, by analyzing images of the fruit. The system captures high-resolution images of mangoes and employs advanced image processing techniques to extract key features, including color, texture, and size. Machine learning algorithms are then utilized to analyze these features and predict the quality of each mango. The project leverages a comprehensive dataset of mango images, encompassing various quality attributes and ripeness stages, to train and validate the predictive models. The potential benefits of this project are multifaceted. Mango producers can benefit from improved quality control, reduced post-harvest losses, and enhanced supply chain management. Consumers gain access to consistently high-quality mangoes. Furthermore, the project contributes to the reduction of food waste and supports sustainable agriculture practices. The system's real-time capabilities can be integrated into various stages of the mango supply chain, from harvest to distribution. However, exemplifies the synergy between agriculture and technology, offering a practical and efficient solution to the challenge of quality prediction in the mango industry. It demonstrates the capacity of image processing and machine learning to enhance the agricultural value chain, ensuring that consumers can savor the highest quality mangoes while fostering sustainability and reducing food waste.