There is an increasing need for effective food picture identification systems due to the rising popularity of social media and mobile applications that are centred on food and nutrition. We give a comprehensive study on the use of You Only Look Once (YOLO), a cutting-edge object detection technique, for food image recognition in this research paper. Yolo is a preferred method for applications requiring food recognition because of its real-time processing capabilities and capacity to find many objects in a single pass. We begin by going through the value of food picture recognition in a number of areas, such as dietary tracking, food recommendations, and menu analysis in restaurants. The technological details of YOLO and its modifications for food image identification are then covered. Our study addresses issues with variable food appearances, portion sizes, and occlusions frequently seen in food photographs by optimising pre-trained YOLO models on food-specific datasets. We also look into how training methods, data augmentation approaches, and model designs affect recognition performance. We go over the practical applications of such an application and possible use cases, such as calorie estimate, nutritional monitoring, and meal planning. The usefulness of YOLO-based models for food image recognition is demonstrated by our experimental results, which show that these models can deliver precise and effective answers for a range of food-related tasks. This study adds to the body of information on deep learning-based image identification and provides helpful information for the creation of useful food recognition systems.