Code smells, defined as detrimental patterns and design choices in software development, significantly impact various aspects of Software Quality, such as maintainability, reuseability, and stability. These harmful effects can disrupt the software development cycle and result in a waste of development and managerial resources. Although code smell prediction has attracted considerable attention in recent years, the existing literature still shows certain limitations. In this thesis, we propose a Homogeneous Stacking Classifier to predict the presence of nine different types of code smells. To evaluate the performance of our proposed model, we compare it against state-of-the-art machine learning techniques that have proven to perform well in current research. Results show that our proposed approach statistically significantly outperforms the other models across most cases therefore, affirming its efficacy in code smell prediction.