Efficiently detecting faults in steel plates is essential for maintaining the safety and dependability of structures and industrial machinery. Timely identification of faults mitigates further damage and averts exorbitant repair costs. This study delves into the efficacy of employing ensemble machine-learning classifiers for fault detection in steel plate manufacturing processes. Specifically, five powerful machine learning models—Random Forest (RF), AdaBoost, Decision Tree, Support Vector Machines (SVM) and Naive Bayes —are investigated in this study. The ensemble models (i.e., RF and AdaBoost) harness the collective power of multiple weak learners to enhance discrimination capacity. Evaluation is conducted using a publicly available dataset comprising seven distinct fault types: Pastry, Z_Scratch, K_Scratch, Stains, Dirtiness, Bumps, and Other_Faults. Results demonstrate Random Forest achieving the highest AUC of 0.942, with an accuracy of 0.771 and balanced F1 score, compared to the other models. This comprehensive investigation enhances fault detection efficacy, fostering informed decision-making in steel plate manufacturing processes.