<p>In recent years, waste management has gained critical importance in health centers due to the risks posed by medical infectious waste. Medical and diagnostic laboratories, as primary generators of infectious waste, require proper waste management to prevent irreparable damage to people and the environment. This research presents a bi-objective mathematical model aimed at minimizing total network costs and contamination risks from laboratory infectious waste, integrating the concepts of agility and green supply chain. The model incorporates uncertainty in key parameters through fuzzy robust optimization and is solved using the extended goal programming approach. A real case study in Iran validates the model, demonstrating its ability to optimize costs and minimize contamination risks. Sensitivity analyses were conducted on three key aspects: penalty coefficients in the fuzzy robust optimization model, the absence of recycling centers in the network, and changes in the importance coefficient of objective functions. The results highlight the significant impact of these parameters on the network’s costs and risks, with recycling centers playing a critical role in reducing costs. These findings provide practical insights for decision-makers to design efficient, sustainable reverse supply chain networks. Suggestions for future research directions are also provided.</p>