Integrating artificial intelligence (AI) in the pharmaceutical industry has delivered transformative outcomes across the value chain, with documented efficiency gains of 40–75% in early-stage drug discovery and potential economic value of $60–110 billion annually. However, this technological revolution presents profound ethical challenges that require systematic consideration. This review examines the ethical dimensions of pharmaceutical AI applications through quantitative and qualitative analysis of key domains: data privacy, where re-identification risks reach 99.98% with just 15 demographic attributes; algorithmic bias, with performance disparities of 8–23% across demographic groups; transparency limitations of black-box models; and evolving regulatory frameworks across major markets. We analyse implementation case studies demonstrating how structured ethical assessment frameworks have reduced bias incidents by up to 76% while improving model performance. Technical solutions, including federated learning, differential privacy, and explainable AI approaches, enable pharmaceutical companies to address ethical concerns without sacrificing innovation, achieving up to 94% of centralized model performance while enhancing privacy protection. As AI integration accelerates, our proposed multi-stakeholder governance model provides actionable guidance for pharmaceutical companies, regulators, healthcare providers, and technology developers navigating this complex ethical terrain while maximizing AI's potential for improving global health outcomes.