Malware remains a pervasive and evolving threat to cyber security. The rapid proliferation of new malware variants requires innovative solutions for timely identification and classification. This thesis presents a comprehensive study focused on the development of a machine learning model to address this challenge. The primary objective of this research is to create a machine learning model for malware identification and classification that prioritizes interpretability. The model aims to provide clear insights into the decision-making process, allowing security analysts to understand the features and characteristics that drive their classifications. This approach is essential for building confidence in automated cybersecurity systems. Firstly, I did extensive research on the state-of-the-art of interpretable machine learning models applied to malware identification and categorization.