This work attempts to classify Indian food items through advanced computer vision techniques. We used a custom dataset consisting of up to 100 high-resolution images for each of the 20 unique Indian dishes identified from Google Images. Through the implementation of object detection models such as YOLOv7, YOLOv8, and YOLOv9, which are all real-time efficiency-based, we analyzed their performances through the use of convolutional neural networks (CNNs). To speed up data preparation, we employed Roboflow’s AutoDistill: a powerful technique for automating the labeling of a dataset with a powerful data labeling method. AutoDistill leverages massive foundational models to create labeled data based on color and shape and ultimately uses these to train smaller, faster models fine-tuned to specific tasks. All in all, by fully automating labeling, high-quality datasets can be built much faster. Our results demonstrate that the YOLOv9 model is one of the state-of-the-art models that ensures perfect accuracy and, thus, the best for monitoring food quality control applications. Thus, this research opens a new horizon in computer vision applied in culinary fields, providing for practical tools in diet monitoring, health assessment, and food safety.