Modern printed circuit board (PCB) manufacturing demands rigorous quality control, as even minor defects can lead to failures in electronic devices. Traditional visual inspection and rule-based automated optical inspection (AOI) methods are often slow, error-prone, and struggle to keep up with complex PCB designs. This paper presents an AI-driven defect detection framework that leverages computer vision and convolutional neural networks (CNNs) to automatically identify PCB defects from high-resolution images. We detail the underlying CNN architecture and explain how it learns to recognize subtle anomalies such as soldering errors, misalignments, and surface imperfections that might be missed by human inspectors. A novel case study is included in which a CNN model is trained and tested on a representative PCB dataset containing various defect types (e.g., missing holes, mouse bites, open circuits, shorts) to evaluate detection accuracy. The proposed approach achieves high defect recognition rates and significantly reduces inspection time, thereby improving production efficiency. We also compare our method against existing techniques, highlighting improvements in mean average precision (mAP) and false detection reduction. This research demonstrates the potential of AI-driven computer vision in revolutionizing PCB quality control by providing fast, accurate, and scalable defect detection, ultimately reducing scrap and rework in manufacturing. Key challenges for real-world deployment are discussed, and future directions – including model improvements, integration with production lines, and use of advanced deep learning architectures – are outlined.