With the rapid increase in malware threats, robust classification methods have become essential to protect digital environments. This study conducts a comparative analysis of machine learning and deep learning methods for malware detection. A variety of models are used from both machine learning and deep learning paradigms to determine their effectiveness in distinguishing malware. To further refine the models, several feature selection techniques are applied to reduce the dimensionality of the data and enhance performance. Performance metrics, including accuracy, precision, recall, and F1-score is used to evaluate each model. The findings indicate that while deep learning approaches generally provide higher detection accuracy, feature selection methods contribute significantly to improving machine learning models in terms of performance and computational efficiency. This analysis offers valuable insights into the balance between model complexity and effectiveness, providing practical recommendations for implementing malware classification systems in real-world applications.