Healthcare requires efficient delivery of supplies, but things often change unexpectedly. In the field of healthcare logistics, transportation efficiency plays a fateful role in ensuring the timely delivery of medical supplies, equipment, and personnel. However, the dynamic nature of the healthcare environment brings complexities, such as unexpected changes in demand, transportation disruptions, and differing priorities. This research highlights a novel approach to address these challenges through an innovative model designed for uncertain multi-objective fractional transportation problems with mixed constraints (UMOFTPMC), which aims to address the complexities of healthcare logistics and resource allocation has to be optimized. By integrating uncertain parameters and multiple objectives – such as minimizing transportation costs, maximizing service quality, and ensuring timely delivery – the proposed model provides a comprehensive approach to healthcare transportation management. Through a series of computational experiments and case studies, the efficacy of the mixed-constraint UMOFTPMC model in healthcare logistics is demonstrated by providing robust, efficient, and adaptable transportation solutions according to the unique demands and uncertainties underlying healthcare systems and exhibiting its ability to bring revolution. Using advanced computational methods and analyzing real-world scenarios, the study elucidates the complexities of healthcare transportation and proposes strategies to increase efficiency, adaptability, and flexibility in healthcare supply chain operations.