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The PCB line defect detection system utilizes a ground-breaking technology for detecting and locating faults within the conducting tracks of printed circuit boards (PCBs), which are critical components in electronic devices. Traditional inspection procedures are time-consuming and might be inaccurate. The system analyses high-resolution pictures of PCBs acquired with specialized cameras using powerful computer vision methods and image processing algorithms. The programme analyses these photos to detect anomalies in the conductive lines such as fractures, short circuits, or irregularities. Machine learning algorithms are used to improve the system's capacity to detect various sorts of errors. Image capture, pre-processing, defect detection, learning algorithm integration, the localization of operations user interface, and automatic reporting are among the system's key characteristics. The system ensures the creation of dependable and high-quality PCBs by providing efficiency, precision, cost-effectiveness, and dependability by identifying faults at an early stage. The PCB line defect detection program provides an innovative product for the electronic manufacturing sector, addressing the demand for PCB inspection that is both quick and precise to maintain the integrity of electronic products. The technology offers a cost-effective alternative in comparison to manual inspection approaches by merging modern visual analysis methods and artificial intelligence models, minimizing the requirement for considerable human labor.