Malware detection remains an enduring challenge in cybersecurity as malicious software grows more sophisticated. Effective feature selection is critical for machine learning-based malware detection systems to maximize accuracy and efficiency. This research paper evaluates feature selection techniques to understand their impact on malware detection performance. We investigate multiple methods for selecting the most informative features from malware datasets to train robust detection models like the Random Forest classifier. Our analysis provides insights into optimizing feature selection to enhance malware detection, a key capability as malware continues evolving amid the digital landscape. By focusing on feature selection, this work aims to advance malware detection research and improve cybersecurity through more performant machine learning approaches. We present empirical comparisons of feature selection techniques and make recommendations for selecting appropriate methods based on factors like dataset characteristics and detection objectives. Our findings highlight promising directions for continued research into optimized feature engineering for malware detection systems.