This study presents an AI-based image processing system for detecting faulty cable soldering on printed circuit boards (PCBs)—a recurring quality concern in electronic manufacturing. The primary contribution is a dual-validation decision logic that simultaneously evaluates cable color sequence and soldering position, enabling more reliable OK/NOK (compliant/noncompliant) assessment than conventional classification-only methods. Four deep learning architectures—VGG16, ResNet-50, MobileNetV3, and YOLOv11-Cls—were trained on a custom-labeled dataset collected in an industrial pilot setting. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics, and real-time feasibility was assessed with an independent test set of 212 images. Of these, Visual Geometry Group 16 (VGG16) achieved the highest accuracy (97.5 %) and perfect recall (1.00) for faulty samples and maintaining an average response time of approximately 700 ms, indicating consistent and reliable operation for real-time inspection. The results demonstrate that dual-validation deep learning-based inspection strategies can deliver robust defect detection under limited training data and low feature variability. With 97.5 % accuracy and zero false negatives for defective products, the framework demonstrates production-ready performance for industrial PCB quality control applications.