The manual assessment and classification of egg quality, an integral process in poultry farming, is hampered by subjectivity and inconsistency, leading to errors in grading and suboptimal preservation that diminish egg freshness and economic value. To address these critical limitations, this study introduces the QUACK-EGG (Quality Assessment and Classification for Keeping Egg Growth) system, a technology-assisted, structured approach designed to enhance the accuracy, consistency, and efficiency of quality control. The research utilized the ADDIE Model Methodology within an iterative, user-centered framework, with an evaluation involving eight quality control practitioners. The system was assessed across three dimensions of the System Usability Scale (SUS): Functionality, Accuracy, and Acceptability. Results showed an overall SUS score of 75.83 ("Good to Excellent Usability"), with the Accuracy dimension scoring an Excellent 85.00. Performance metrics confirmed high classification accuracy (4.88/5.00) and superior Decision Support (4.80/5.00) through its innovative "Growth Monitoring" feature, which proactively predicts quality decay. Comparative analysis against manual methods confirmed QUACK-EGG's decisive advantage in consistency and time efficiency. Findings indicate that QUACK-EGG successfully shifts quality control from subjective judgment to a standardized, data-driven, and predictive process, offering a viable solution to maximize egg preservation and improve operational confidence in the modern supply chain.