This study evaluates the question-answering capabilities of Large Language Models (LLMs) in Urdu, addressing a critical gap in low-resource language processing. Four models GPT-4, mBERT, XLM-R, and mT5 are assessed across monolingual, cross-lingual, and mixed-language settings using the UQuAD1.0 and SQuAD2.0 datasets. Results reveal significant performance gaps between English and Urdu processing, with GPT-4 achieving the highest F1 scores (89.1% in English, 76.4% in Urdu) while demonstrating relative robustness in cross-lingual scenarios. Boundary detection and translation mismatches emerge as primary challenges, particularly in cross-lingual settings. The study further demonstrates that question complexity and length significantly impact performance, with factoid questions yielding 14.2% higher F1 scores compared to complex questions. These findings establish important benchmarks for enhancing LLM performance in low-resource languages and identify key areas for improvement in multilingual question-answering systems.