Semiconductor production demands high-quality control to detect faulty wafers early on in the production process. Manual inspection and rule-based systems are conventional methods that are time consuming and error-prone. This research investigates machine learning (ML) based wafer detection on a dataset of 590 sensor readings per wafer, with wafers being labeled as good (+1) or faulty (−1). Several traditional ML models, such as Logistic Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest, are tested for defect classification effectiveness. The processing of data includes handling missing values by dropping features with high missing data and using median imputation. Feature selection is done through SHAP (Shapely Additive Explanations) analysis and correlation filtering to select only the most important sensor readings. Feature scaling is done to maintain consistency in data distribution. For handling the class imbalance in the dataset, SMOTE (Synthetic Minority Over-sampling Technique) is employed to create synthetic samples for the minority class to enhance model learning. Once trained, the models are evaluated on the basis of accuracy, precision, recall, F1-score, confusion matrix, and SHAP-based explainability analysis. SVM and Random Forest perform better compared to other models with 97–99% accuracy, and KNN does not perform well because of high dimensionality. The research showcases how ML is able to automate defect detection, increase production efficiency, and minimize human inspection errors. Work for the future encompasses ensemble learning optimization, real-time deployment, and semi-supervised learning optimization for enhanced defect classification in the semiconductor industry.