The urgent issue of food waste is addressed in this thesis using a novel system. The main goal of the study is to provide a unique method for reducing kitchen food waste using a recommendation system and image processing-driven food analysis. For the image processing-driven food analysis, the researchers employed the YOLOv5 (You Only Look Once version 5) model, a state-of-the-art deep learning model renowned for its real-time object detection capabilities. YOLOv5 enables the system to accurately identify and classify various food items within images, contributing to a more comprehensive understanding of food waste patterns. Simultaneously, a content-based recommendation system was implemented using cosine similarity. This recommendation system analyzes the ingredients of food items detected by the YOLOv5 model and compares them to suggest recipes that utilize similar ingredients. Cosine similarity, a mathematical measure of similarity between two vectors, is applied to determine the likeness between ingredient profiles. This innovative approach enhances the system's ability to suggest relevant recipes that align with the available ingredients in a user's kitchen. The importance of this study rests in its creative strategy for addressing the pressing problem of food waste. The "GoGreen" technology provides a workable approach to lowering kitchen waste by smoothly merging image processing for food analysis and a well-structured suggestion system. The results of this study offer useful information for both homes and culinary experts as food waste continues to pose ethical and environmental problems.