With the advancement of AI technology, the generation of content has become easier and more accessible. In such a scenario, it is difficult to differentiate between human-generated text and AI-generated text. To address this concern, our methodology proposes an intelligent system capable of analyzing text files and classifying unique writing styles using stylometric analysis. This work also compares various clustering algorithms, including k-means, k-means++, hierarchical, and DBSCAN, utilizing silhouette scores as performance metrics. This ensures the effectiveness of our system in distinguishing between similar and dissimilar writing styles based on advanced linguistic and structural features of text. Our tools separate text of different styles and clustered it together to provide a valuable solution for detecting plagiarism across multiple document files by grouping it. Our system demonstrated its efficacy by successfully identifying two distinct writing styles within a document. This highlights the practical application and accuracy of our approach in effectively addressing complex challenges associated with stylistic differences and the potential for plagiarism detection.