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The creation of digital content and the easy accessibility of information have led to a surge in academic and textual plagiarism. Plagiarism detection in multiple languages is essential to maintain the integrity of academic and literary works. In the context of the Urdu language, there is a growing need for effective plagiarism detection methods that are tailored to its unique linguistic characteristics. Existing Urdu plagiarism detection tools often rely on external sources or lack robustness in handling intrinsic forms of plagiarism, where the copied content is slightly modified or paraphrased. This research aims to bridge this gap by developing an intrinsic plagiarism detection system for the Urdu language, using a combination of machine learning, ensemble learning and Multi-Layer Perceptron (MLP). Furthermore, to train and evaluate our plagiarism detection models, we manually curate a corpus comprising a substantial …