Malicious software must be detected in order to protect sensitive data and systems in the digital era, as sophisticated malware is posing serious risks to cybersecurity. By examining supervised machine learning approaches with a particular focus on Random Forest, Logistic Regression, and Decision Trees, this research proposes a data-driven approach to malware detection. These algorithms are trained to recognize patterns indicating malware by using labeled datasets containing four types of malwares, Ransomware, Trojan, Virus, and Worm. The performance of these algorithms is comprehensively investigated in the paper, with comparisons made between their accuracy, precision, recall, and F1-score. Based on the experimental results, Random Forest (96% accuracy) performed better in terms of robustness and accuracy of detection than both Logistic Regression (91%) and Decision Trees (84%). Logistic Regression provided faster computation at the expense of less accurate detection. Decision trees, while relatively simple to comprehend, performed moderately and they overfit the data. The study’s conclusion highlights the significance of choosing the appropriate model in accordance with particular cybersecurity requirements, outlining the advantages and disadvantages of every approach as well as their practical applicability in real-time malware detection systems.