The proliferation of Large Language Models (LLMs) has introduced significant challenges regarding algorithmic bias, privacy, and the authenticity of digital content. While detection mechanisms for English are maturing, low-resource languages like Urdu—spoken by over 100 million people—require dedicated research. In this paper, we present a technical framework for Urdu AI-generated text detection developed for the *ACL shared task. We propose a hybrid pipeline that combines TF-IDF Character N-grams with a custom stylometric feature extractor designed to capture unique Urdu linguistic markers, including repeated word ratios, punctuation density, and formal function markers. Using a Linear Support Vector Machine (SVM) optimized via Stochastic Gradient Descent (SGD), our system achieves a balanced accuracy and F₁-score of 87.80% on a dataset of 6,800 records. Our results demonstrate that a computationally efficient, classical machine learning approach—prioritizing stylistic signals over heavy preprocessing—remains highly effective for distinguishing between human-written and AI-generated Urdu text.