The quality of weld seams is critical for the integrity and longevity of welded structures. Operator errors during the gas metal arc welding process can lead to significant quality deviations and defects, affecting the safety and reliability of manufactured products. This study presents a comprehensive approach for detecting operator errors by analyzing welding current, arc voltage, and wire feed speed data. We developed models capable of identifying operator errors with high accuracy by applying advanced feature engineering techniques and utilizing machine learning algorithms such as support vector machines and artificial neural networks. The primary research question addresses how operator errors in the gas metal arc welding process can be detected in real time using existing sensor data without the need for additional hardware. The paper is structured to provide a thorough background, detailed methodology, extensive results, and critical discussion. The results demonstrate that real-time monitoring and error detection are feasible without additional sensor technology, enhancing production quality and efficiency.