Low-resource languages like Urdu suffer from limited high quality parallel data for machine translation. We introduce a curated English–Urdu corpus of 80,749 high-fidelity sentence pairs across 18 diverse domains, built via ethical collection, manual alignment, deduplication, and strict length-based filtering (AWCD ≤ 5). The corpus is converted into a bidirectional SFT dataset with bilingual (English/Urdu) instructions to enhance prompt-language robustness. Fine-tuning Llama-3.1-8B-Instruct (Llama-FT) and UrduLlama 1.1 (UrduLlama-FT) yields major gains over the baseline. sacreBLEU scores reach 24.65–25.24 (En→Ur) and 76.14–77.97 (Ur→En) for Llama-FT, with minimal sensitivity to prompt language. Blind human evaluation on 90 sentences per direction confirms substantial perceptual improvements. Results demonstrate the value of clean parallel data and bilingual instruction tuning, revealing complementary benefits of general SFT versus Urdu specific pretraining. This work provides a reproducible resource and pipeline to advance Urdu machine translation and similar low-resource languages.