The healthcare supply chain is a complex, data-intensive ecosystem that requires advanced analytics and real-time decision-making to ensure efficient pharmaceutical distribution. However, the adoption of artificial intelligence (AI) in healthcare logistics presents significant challenges, including ethical concerns, bias in predictive models, and regulatory compliance. This paper explores the role of Explainable AI (XAI) and Federated Learning (FL) in enhancing transparency, security, and fairness in healthcare supply chain intelligence. XAI provides interpretability in AI-driven decision-making, allowing supply chain stakeholders to understand, audit, and validate model outcomes. This is crucial for ensuring ethical AI adoption, particularly in pharmaceutical distribution, where biased models can lead to disparities in drug availability and accessibility. Federated Learning, a decentralized approach to machine learning, enables collaborative data analysis across different entities while preserving data privacy. This is particularly important for global pharmaceutical companies navigating stringent data protection regulations such as HIPAA, GDPR, and FDA guidelines. The integration of XAI and FL addresses key challenges in healthcare logistics, including demand forecasting, counterfeit drug detection, and equitable drug distribution. By improving model transparency, mitigating biases, and ensuring compliance with global regulations, these technologies provide a scalable and ethical framework for AI-driven pharmaceutical supply chain intelligence. This paper highlights real-world applications, regulatory considerations, and best practices for deploying XAI and FL in a responsible and effective manner.