<p>The purpose of this research is to harness the power of Artificial Intelligence (AI) for insights into FDA recall patterns. The study aims to explore the characteristics of FDA recall data from 2018 to 2023 to gain insights into the patterns, and safety/quality issues associated with the recalls. A retrospective data analysis was conducted using Artificial Intelligence (AI) to analyze 235 drug&#xa0;recalls, efficiently processing extensive and unstructured data, revealing significant statistics, prevalent causes, patterns in formulation/drugs, and instances of guideline violations. Textual analysis and Machine Learning (ML) functionalities further facilitate the classification, categorization, and identification of patterns. The study included recalls posted on the USFDA webpage, publicly available on the public domain. The study focused on 235 recall entries of FDA recall data from 2018 to 2023, studying different aspects of the recalls. The highest number of recalls was found in the year 2021, which was around 29.4% of the total recalls. The percentage of terminated recalls varied annually, ranging from 4 to 25% over the years. Most common reason for recalls was identified as microbial contamination (22% of total recalls). Under product category, Drugs/Pharmaceuticals contributed significantly to recalls each year (54% of total recalls). The most frequently recalled API was Metformin, which constituted 7% of total recalls. Prescription products constituted highest recalls (56%) as compared to non-prescription products (44%). Majority of recalls originated from companies based in the USA, which was around 68%. The research highlights AI’s ability to swiftly analyze large datasets, improving efficiency compared to manual methods. This study demonstrates the potential of AI and ML for continuous learning and proactive risk mitigation within the pharmaceutical sector, highlighting the concept of AI as an efficient analytical tool for data identification, classification and analysis.</p>