<p>Effective management of healthcare waste is essential for protecting public health and minimizing environmental harm. The complexity of healthcare waste management (HWM), characterized by multiple criteria and inherent uncertainty, demands robust MCDM methods. This study proposes a novel decision-support framework that integrates the MEREC and the MULTIMOORA techniques within the CFFS environment. CFFS, which encompasses FFS and IVFFS, addresses uncertainties and vagueness more effectively than traditional fuzzy frameworks like CIFS or CPFS. MEREC is employed to derive objective criteria weights, while the threefold MULTIMOORA technique, comprising the Ratio System, Reference Point, and Full Multiplicative Form, provides a comprehensive and robust ranking of alternatives. To further enhance decision reliability, the Borda rule is employed for final aggregation of rankings. The proposed framework is implemented in a real-world case study involving the evaluation of four HCW disposal methods: autoclaving, microwave treatment, chemical disinfection, and incineration across ten criteria involving economic, technical, environmental, and regulatory dimensions. Comparative analysis demonstrates the model’s superiority, consistency, and reliability over existing methods such as CFF–TOPSIS. Sensitivity analysis across diverse weighting scenarios confirms its robustness. The study primarily highlights the effectiveness of the proposed CFF–MEREC–MULTIMOORA framework, and autoclaving is identified as the most preferred healthcare waste treatment option among the considered alternatives, based on the extended MCDM evaluation under uncertainty.</p>