Accurate detection and decoding of barcodes in identity documents is crucial for applications such as security, healthcare, and education, where reliable data extraction and verification are essential. However, building robust detection models is challenging due to the lack of diverse and realistic datasets, an issue often tied to privacy concerns, and the wide variety of document formats. Traditional tools like Faker rely on predefined templates, which makes them less effective in capturing the complexity of real-world identity documents. In this paper, we introduce a new approach to synthetic data generation that uses LLMs to create contextually rich and realistic data without relying on a predefined field. Using the vast knowledge LLMs have about different documents and content, our method creates data that reflect the variety found in real identity documents. This data is then encoded into barcode and overlayed on identity document templates. Our approach simplifies the process of creating a dataset, eliminating the need for extensive domain knowledge or pre-defined fields. Compared to traditional methods such as Faker, LLM-generated data demonstrates greater diversity and contextual relevance, leading to improved performance in barcode detection models. Our experiments show that using LLM-generated data improves the detection mAP@0.5 by 4.2%, making it a scalable, privacy-first solution that significantly enhance machine learning for automated document processing and identity verification.