The Severity Defect Intelligence Detection System is a deep learning–based solution designed to automatically identify defects in manufacturing products using image data. It addresses the limitations of manual inspection, which is time-consuming and prone to human error, by using image preprocessing techniques like resizing, normalization, and noise removal to enhance input quality. A Convolutional Neural Network (CNN) is then applied to extract visual features such as edges, textures, and patterns, enabling the system to classify products as defective or non-defective based on a probability threshold. The model is trained on labeled datasets and evaluated using metrics like accuracy, precision, recall, and F1-score, ensuring reliable performance. This system improves efficiency, reduces human effort, and enhances quality control in industrial environments, although its effectiveness depends on data quality and may face challenges with small defects or poor imaging conditions.