Given environmental challenges such as global warming and resource depletion, green supply chain management—with a focus on reducing energy consumption and greenhouse gas (GHG) emissions—has gained increasing prominence. In the pharmaceutical industry, managing the product life cycle and addressing accessibility challenges in developing countries, alongside combating counterfeit drugs to preserve the integrity of the healthcare system, have become top priorities. To enhance pharmaceutical credibility and optimize supply chain management, this study proposes a blockchain-based approach. In this regard, a multi-objective optimization model is formulated as a Mixed-Integer Linear Programming (MILP) problem, aiming to simultaneously maximize total profit and fulfillment rate while minimizing carbon emissions. The model is solved using a hybrid approach that integrates the Metric LP method and the Weighted Sum method, modeling the supply chain as a three-echelon network consisting of manufacturers, distributors, and pharmacies. Numerical results obtained from a case study demonstrate that proper management of objective coefficients facilitates the attainment of an optimal state. Furthermore, the findings indicate that while product transfers play a crucial role in balancing inventory levels, suboptimal transfer management can significantly increase both operational costs and environmental impacts.