AI-Driven Stability Testing in Pharmaceutical Industry

Traditional stability testing in the pharmaceutical industry is a critical but resource-intensive, time-consuming, and error-prone process. This review project explores the transformative impact of Artificial Intelligence (AI) on pharmaceutical stability testing, detailing how computational methods are revolutionizing the field. The primary aim is to review and discuss the AI-driven methodologies, applications, and strategies that enhance the accuracy, efficiency, and predictive capability of stability studies.This report provides a comprehensive overview of AI's role, covering predictive modeling for shelf-life estimation, degradation pathway analysis, and formulation optimization. It examines the specific types of AI methods employed, including Machine Learning (ML), Deep Learning (DL), and Digital Twins. Furthermore, the review details the integration of AI with essential instrumentation (such as Stability Chambers, HPLC, and DSC) and specialized software (like Axiologo Stability Modeller, FormSCI, and KNIME) to create a modern, automated workflow.The key findings indicate that AI integration significantly accelerates testing timelines, reduces experimental costs and human error, and provides deeper, data-driven insights for proactive decision-making. This shifts the paradigm from reactive testing to a more efficient, predictive, and continuous approach.While challenges related to high implementation costs, data quality dependence, and regulatory validation remain, the adoption of AI is fundamentally reshaping pharmaceutical quality control. This data-driven transformation is poised to become a new standard, accelerating drug development and ensuring the consistent delivery of safer, more effective medicines to patients.