This study evaluates ChatGPT’s potential to generate accurate, efficient, and standards-compliant MARC 21 records for Arabic materials. It aims to streamline Arabic bibliographic cataloging workflows and enhance library operational efficiency. The methodology involves prompting ChatGPT to create records for a representative sample of Arabic materials, followed by a comparative analysis against human-generated MARC 21 records to identify any discrepancies in adherence to MARC 21 cataloging rules. The findings contribute to discussions on ongoing advancements in generative AI technologies in bibliographic metadata generation and cataloging standards compliance, and their influence on future models and frameworks in library and information science.