This project presents a hybrid plagiarism detection system designed to identify both direct and paraphrased content. It combines traditional similarity measures like cosine similarity and Jaccard similarity with a supervised machine learning model that analyzes deeper linguistic features using NLP. While similarity metrics handle exact text matches, the ML model detects more complex forms of plagiarism. This approach improves accuracy and adaptability, making the system suitable for academic and content verification purposes.