<p>Eggs represent a fundamental food product worldwide due to their high protein content and many essential nutrients. The eggs’ delicate shells are susceptible to cracking, creating bacterial contamination opportunities and introducing reduced shelf life and monetary losses for agricultural business operations. Maintaining egg quality remains vital because it protects human safety and keeps production operations economical. Conventional quality control through manual methods has proven laborious and pruned to human mistakes while becoming unfeasible for industrial-scale production. This research develops an economical automated system that detects egg cracks while executing condition-based labeling and overcoming conventional inspection methods’ drawbacks. A machine vision system combines multiple computer vision methods that use color-based segmentation, morphological filtering, edge detection with the Canny method, and connected component analysis for digital surveys. The system classifies eggs into two classes: cracked and healthy; then, good condition eggs are labeled and sorted while cracked eggs are discarded. Experimental results achieved 94.89% classification accuracy and a 3-second from laying the egg to labeling, which provides potential industrial adoption. The proposed system offers benefits for reliable food safety measures and uses waste prevention features to help accelerate automated egg processing quality control. This system executes live analysis through a different approach. It analyzes eggs entering the testing area while they depart the post-harvest phase without necessitating any pre-existing datasets for training. The real-time functionality enables smooth operations in quality control procedures to support large-scale industrial use.</p>