In this study, we examine the application of convolutional neural networks (CNNs) for wafer pattern classification, with a focus on enhancing training efficiency and model performance. To achieve this, particle swarm optimization (PSO) is employed to improve the model performance while reducing its complexity, a critical factor in production environments. By minimizing the number of layers, the proposed method accelerates training, reduces resource consumption, and enhances defect detection accuracy. Wafer failure patterns are classified into four categories: vertical, rectangular, edge, and horizontal. The approach achieves an impressive F1-score of 0.988, significantly surpassing the traditional CNN’s score of 0.83. By integrating PSO, the method considerably improves the visual inspection process for hard disk drives, contributing to high-quality production. This optimization not only streamlines workflows but also enables manufacturers to address issues more rapidly, aligning with Industry 4.0’s objectives of automation and intelligent monitoring.