The main contribution of the present study is to develop a novel hybrid machine learning model to enhance the defect prediction in industrial manufacturing processes. In this work, the model integrated four base models of XGBoost, LightGBM, CatBoost, and an artificial network, whose features are modeled with Random Forest (RF) as the metamodel using a stacking ensemble approach. For this study, the industrial data from Kaggle were used, and for their extensive and detailed hyperparameter optimization with Optuna, we greatly improved the prediction performance with the model. In the context of this study, key challenges like the data imbalance and the selection of the important features were solved using data balancing techniques like SMOTE and random forest-based analysis for selecting the most important input features. The hybrid model generated great results, which were quite better than the traditional single models, with an accuracy of 96.06% and precision, recall, and F1 scores of 95.10%, 97.32%, and 96.20%, respectively. The real-world applications of this model can be many by accurately and timely predicting defects in industrial environments. All results are reliable and interpretable due to the usage of robust data preprocessing methods, including feature standardization and correlation analysis. This study's results will have a significant impact on such tasks as defect management in manufacturing, as it provides a very scalable solution to enhance product quality, minimize operational cost, and improve process efficiency. This research illustrates the promise of hybrid machine learning methods in tooling manufacturing process optimization and the performance of industry.