<p>Pharmaceutical logistics focuses primarily on cost and individual health, while environmental and social aspects are often overlooked. Although electric vehicles reduce pollution in drug delivery, they also create new challenges such as congestion at charging stations and increased electricity demand. Additionally, the lack of drug tracking systems and the risk of data manipulation by fraudsters introduce further complications in this supply chain. To address these issues, this paper proposes a sustainable drug supply chain incorporating blockchain technology and solar-powered vehicles. The number of blocks in the blockchain and transactions are determined based on the medicines transferred and their recording times. Both solar-powered and conventional vehicles are employed to transport medical goods. The weather conditions and shading affect solar energy harvesting. The distribution centers are considered as home healthcare centers, ensuring timely delivery of medical items. Moreover, this paper considers demand under uncertainty. To deal with this, a data-driven robust optimization model (DDROM) is proposed, which uses support vector clustering to construct the uncertainty set. The augmented <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varepsilon\)</EquationSource> </InlineEquation>-constraint method is applied to compare the DDROM with the box-polyhedral robust model. The results demonstrate that the DDROM outperforms the Box-polyhedral model based on the mean ideal distance and spread of non-dominance solution metrics. Furthermore, by converting the multi-objective functions into a single-objective function, the robustness of the DDROM is examined under varying conservatism coefficients and scenario realizations. As the conservatism coefficient increases, the uncertainty set expands, leading to more conservative solutions. Finally, a sensitivity analysis is performed on the blockchain memory size, showing that the model’s responses align with the expected behavior.</p>