Integrating machine learning into quality control significantly improves defect detection in manufacturing, especially in the automotive industry, where the use of 3D-printed parts is growing. Traditional manual inspection methods are error-prone, which limits inspection efficiency. This study investigates the application of deep learning algorithms for automated visual quality control in the automotive industry. The research focuses on three prominent models—Faster R-CNN, YOLO, and U-Net—analyzing their effectiveness in detecting defects in 3D-printed components. A comparative assessment was conducted between these machine learning models and traditional manual inspection methods. The results indicate that Faster R-CNN achieves high accuracy in defect detection, YOLO offers efficient real-time processing, and U-Net provides precise segmentation of surface anomalies. The findings demonstrate that deep learning-based inspection can significantly enhance quality control by reducing human error, minimizing production downtime, and improving defect detection accuracy. A comprehensive evaluation involving thousands of inspected parts highlights the advantages of AI-driven solutions in modern manufacturing processes. Each of the three models presents unique strengths depending on specific industrial requirements. While Faster R-CNN excels in accuracy, YOLO is advantageous for real-time applications, and U-Net is particularly effective in anomaly segmentation. However, no single model emerges as the definitive best choice, as their suitability depends on factors such as computational efficiency, accuracy demands, and real-time processing constraints. This study underscores the potential of machine learning algorithms in advancing automated quality control systems.