With the rapid evolution of network security threats, effective detection and analysis of malware has become important. This research focuses on new methods for malware detection and analysis using advanced machine learning algorithms. By integrating audited and unaudited programs, we aim to increase the accuracy and speed of malware detection. Our approach involves extracting and using different techniques from various malware samples; thus enabling the model to learn complex patterns and behaviors associated with malware. We investigate the effectiveness of different learning models in malware detection and distribution, including deep learning architectures, clustering and anomaly detection. Additionally, we examine the effectiveness of this model against evasion techniques used by sophisticated malware. Check performance in situations. This research helps develop more effective cybersecurity defenses and provides insights into the use of machine learning to combat malware threats.