This study explores the effectiveness of transformer-based models for Named Entity Recognition (NER) in Urdu legal documents, a critical task in low-resource language processing. Given the legal texts’ specialized terminology and complex syntax, accurate entity recognition in Urdu remains challenging. We developed a legal Urdu dataset that contains 117,500 documents, generated synthetically from 47 different types of legal documents, and evaluated three BERT-based models. XLMRoBERTa, mBERT, and DistilBERT by analyzing their performance on an annotated Urdu legal dataset. mBERT demonstrated superior accuracy (0.999), and its F1 score (0.975) outperforms XLMRoBERTa and DistilBERT, highlighting its robustness in recognizing entities within low-resource languages. To ensure the privacy of the personal identifiers, all documents are anonymized. The dataset for this study is publicly hosted on Hugging Face and will be made public after the publication.