In the contemporary digital landscape, the proliferation of malware presents a significant threat to the security and integrity of computer systems and networks. Traditional signature-based detection methods are increasingly ineffective against the evolving landscape of sophisticated malware variants. Consequently, there is a pressing need for innovative approaches to malware detection that can adapt to emerging threats in real-time. This research aims to develop a malware detection system using machine learning algorithms. Random Forest classifier and Logistic regression were deployed for the classification of malware based on the features extracted from the CIC-MalMem-2022 dataset. The Malware detection system model was implemented using the Python programming language and evaluated using major performance metrics like F1-score, precision, recall, and accuracy to assess the model’s performance. A comparison between the logistic regression model and the random forest model showed that the Random Forest model approach performed better than the logistic model in detecting malware, achieving accuracies of 98% and 94% respectively. In summary, the report concludes that the developed Malware Detection System using Machine Learning, specifically the Random Forest and Logistic regression models, shows promise in effectively detecting malware and highlights the importance of leveraging Artificial Intelligence for combating malware threats in the computing community.