The pharmaceutical industry, characterized by complex manufacturing systems and resource-intensive development cycles, generates significantly higher emissions and waste compared to other sectors—up to 55% more per revenue dollar than the automotive industry. Approximately 30% of raw materials are lost during drug production, highlighting urgent sustainability challenges. In response to growing environmental pressures, this study investigates the potential of Artificial Intelligence (AI) technologies to reduce waste and enhance sustainability within the pharmaceutical sector. Through a combination of quantitative data analysis and qualitative insights from major pharmaceutical companies—including Novo Nordisk, Pfizer, GSK, and AstraZeneca—this research explores how AI can transform various stages of the pharmaceutical value chain to improve efficiency and environmental performance. The study identifies three core applications of AI in the industry: business process optimization, waste classification and recycling infrastructure, and decision and policy support. Drawing from the principles of industrial ecology and circular economy, it evaluates how AI can be used to reduce defective drug batches, improve the recyclability of pharmaceutical waste, optimize resource flows, and enhance supply chain efficiency. Case study demonstrates AI’s ability to significantly reduce maintenance costs, avoid production losses, and cut emissions. AI’s role in optimizing pharmaceutical processes at different stages of the pharma value chain – from clinical trial design using digital twin simulations to forecast drug demand and reduce waste, to manufacturing optimization, predictive maintenance, and sustainable supply chain management – enables data-driven decisions, improves efficiency, and minimizes environmental impact. Despite these benefits, the adoption of AI faces numerous barriers including high implementation costs, regulatory challenges, data integration issues, and resistance to change within organizations. Technical limitations, cybersecurity concerns, and the lack of standardized industry frameworks further hinder widespread implementation. The study argues that overcoming these challenges will require coordinated efforts among stakeholders, including public institutions, to provide funding, develop digital skills, and establish policy frameworks that support the responsible use of AI for sustainability. While AI contributes positively to circular and climate-resilient healthcare systems, its energy demands and digital divides must be addressed. The research concludes that AI has the potential to be a pivotal enabler of sustainable transformation in Big Pharma, but its integration must be managed carefully to maximize benefits and minimize unintended consequences. Public investment should focus not only on AI innovation but also on building community readiness for the digital and green transition in healthcare