Published Paper Details:

The preparation and submission of regulatory dossiers such as the electronic Common Technical Document (eCTD), New Drug Applications (NDA), and Abbreviated New Drug Applications (ANDA) remain highly complex, time intensive, and vulnerable to human error. Global regulatory authorities, including the US FDA, EMA, and CDSCO, enforce stringent and region-specific submission standards, resulting in significant operational burden and approval delays. This review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in modernizing regulatory document development across the full submission lifecycle. Particular emphasis is placed on AI-enabled content authoring, automated dossier assembly, semantic consistency checks, predictive compliance analytics, and lifecycle management for both NDA and high-volume ANDA submissions. The integration of AI technologies enhances data consistency, reduces technical deficiencies, and supports real-time regulatory compliance, positioning AI-driven platforms as critical enablers for efficient, transparent, and future-ready regulatory submissions in the era of eCTD v4.0.