<![CDATA[Artificial intelligence (AI) is revolutionizing the pharmaceutical industry by enhancing efficiency, precision, and cost-effectiveness in drug development. This study explores the application of AI in the lifecycle management of generic drugs, focusing on key stages such as active pharmaceutical ingredient (API) synthesis, excipient selection, pre-formulation studies, bioequivalence testing, and regulatory compliance. By leveraging machine learning algorithms, AI facilitates predictive modeling, risk assessment, and optimization of drug formulation processes, reducing time-to-market and improving scalability. Despite significant advancements, challenges such as data quality, algorithm transparency, and infrastructure limitations persist, particularly in resource-constrained settings. This review highlights case studies and emerging technologies that address these challenges, providing actionable insights for pharmaceutical stakeholders. The study also discusses AI's potential to streamline supply chain logistics, enhance accessibility, and ensure regulatory adherence. By integrating AI across all stages of generic drug development, this research underscores its transformative potential in improving drug affordability, accessibility, and patient outcomes globally.]]>
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