This study presents a YOLOv11-based framework for automated assessment of bread freshness over a period of ten days. A comprehensive dataset of 3,600 bread samples collected from 20 bakeries was created to capture preservative effects and spoilage progression, enabling precise edibility grading. An advanced preprocessing pipeline employing YOLOv11-based region-of-interest (ROI) extraction ensured analysis focused on relevant bread portions. The proposed framework achieved strong segmentation and classification performance, with mAP50 scores of 0.965 for both bounding box and mask predictions and an overall classification accuracy of 81%. Notably, it excelled in identifying the extreme freshness stages. Comparisons with texture-based feature extraction methods and conventional classifiers validated its robustness and superiority. These findings demonstrate YOLOv11’s potential as a scalable tool for non-destructive bread freshness monitoring, shelf-life estimation, and automated food quality inspection.