"This project focuses on predicting the efficiency of apparel production lines using machine learning, with the goal of classifying lines into Efficient, Low Efficient, or Inefficient categories. Efficient production is critical in the apparel industry, yet identifying underperforming lines in real time remains a challenge due to the complexity of operational factors like SMV,lead times, and production hours. The project adopts a data-driven approach using a historical production dataset. After preprocessing and exploratory analysis, key features were selected and engineered to enhance model performance. PyCaret, a python based machine learning library, was used to train and compare several classification algorithms, including Logistic Regression, Random Forest, LightGBM, and XGBoost. Based on accuracy, F1-score, and AUC, the XGBoost classifier was chosen as the final model due to its superior performance and robustness. Hyperparameter tuning was applied to optimize the model, which achieved a final accuracy of 79% and a macro F1-score of 0.84.To ensure generalizability, 10-fold stratified cross-validation was used, and the model was tested on unseen data. A Flask web application was developed to make the model accessible to non-technical users. This allows production managers to input planning data and receive immediate efficiency classifications, supporting proactive decision-making. Overall, the project demonstrates how machine learning can enhance operational efficiency in apparel manufacturing. Though effective, the system could be improved by incorporating real- time ERP integration and expanding the dataset for broader applicability."