In the rapidly evolving landscape of tobacco regulation, staying abreast of the latest research, policies, and legal developments is paramount for regulatory professionals. The vast and continuously expanding body of literature on tobacco products presents a unique challenge in accessing, organizing, and harnessing the information available. We propose the establishment of an Artificial Intelligence (AI)-driven Large Language Model (LLM) database specifically tailored to support regulatory professionals in navigating and leveraging published literature on tobacco products. The AI LLM database aims to address the gaps and inefficiencies in accessing and utilizing tobacco products literature by leveraging the power of machine learning and natural language processing techniques. We will provide an overview of the key components and benefits of the proposed database, highlighting its potential to revolutionize the way regulatory professionals engage with tobacco products literature. The AI LLM database will offer a solution by employing advanced algorithms to curate, categorize, and analyze the extensive literature on tobacco products. The database can identify patterns, detect trends, and uncover relationships within the literature, providing regulatory professionals with a deeper understanding of tobacco products' legal, scientific, and public health aspects. Such insights can assist in developing effective regulatory frameworks, evaluating the impact of existing policies, and identifying areas for further research. By harnessing the power of AI, machine learning, and natural language processing, the proposed database has the potential to transform how regulatory professionals access, analyze and utilize published literature. This innovative tool can enhance decision-making, facilitate evidence-based policy development, and support the continuous advancement of tobacco regulation in an increasingly complex and dynamic landscape.