Product inspection is essential for ensuring the quality and dependability of manufactured goods. Traditional manualexamination procedures are time-consuming, subjective, and error prone. As manufacturing complexity and production volumes increase, there is a rising need for automated inspection systems with accurate defect detection and classification. This research presents a deep learning-based quality inspection approach for submersible pump impellers. Three convolutional neural network (CNN) architectures, VGG16, ResNet50, and a custom model, are employed. A graphical user interface (GUI) is developed for real-time inspection. The approach achieves up to 99.8% accuracy in identifying defects, including surface scratches, corrosion, and geometric irregularities. It improves the quality assurance process by reducing manual inspection efforts. The GUI improves usability and decision-making. This study contributes to industrial quality control by introducing a novel deep learning application. Future research could explore advanced techniques like anomaly detection to further enhance system performance and versatility.