The rapid growth of healthcare information systems has led to an increase in the volume of clinical data stored in electronic medical record systems. This data contains valuable information such as patient demographics, vital signs, diagnoses, and medical procedures, which can be used to support clinical decision-making. Determining the appropriate clinical procedure remains a challenge due to the complexity of patient conditions and the potential need for multiple procedures simultaneously. This study aims to develop a robust model for predicting clinical procedures using a multi-label classification approach based on electronic medical record data. The proposed methodology integrates data preprocessing, feature engineering, followed by the implementation of several Machine Learning methods such as LightGBM, XGBoost, CatBoost, Deep Learning Models and combined using an Ensemble Learning method. Various Ensemble Learning techniques, including Simple Average, Machine Learning Average, and Optimized Weight-based combinations, are applied to improve predictive performance. Evaluation is performed using the Micro F1 score and Macro F1 score to assess overall performance. Experimental results show that individual models achieve competitive performance, with LightGBM, XGBoost, CatBoost outperforming the deep learning approach on tabular clinical data. The Ensemble Learning approach further improved performance, with the Optimized Weight with Grid Search Optimization ensemble achieving the highest Micro F1 score of 0.7788 and Macro F1 score of 0.7674. These results also demonstrate that combining multiple models effectively reduces bias and variance while improving generalization. The small difference between Micro and Macro F1 scores indicates balanced performance across labels. In conclusion, the proposed Ensemble Learning-based multi-label prediction model demonstrates robust capabilities in handling complex clinical data and improving prediction accuracy. This study highlights the importance of model combination strategies rather than increasing model complexity. These findings have practical implications for supporting clinical decision-making systems in healthcare settings.