<p>Food waste depletes land, harms biodiversity, and damages ecosystems. As a consequence, it has been identified as one of the major factors that develop severe environmental problems globally. For successful management of food waste, selection of proper and appropriate treatment technology is one of the most significant decision-making issues due to involvement of numerous sustainability criteria and data uncertainty. This paper aims to introduce a hybrid four-stage decision-making methodology for selecting food waste treatment technologies (FWTT) using <i>q</i>-rung orthopair fuzzy soft rough (<i>q</i>-ROFS_<i>ft</i>R) sets. The proposed methodology begins with a weighting algorithm to assess the importance of decision-makers involved in the FWTT decision-making procedure. The second stage aggregates group of decision-makers’ opinions using an improved <i>q</i>-ROFS_<i>ft</i>R aggregation operator, which includes the <i>q</i>-ROFS_<i>ft</i>R Hamacher weighted averaging operator and its geometric variants. In the third stage, the stepwise weight assessment ratio analysis (SWARA) method is extended to the <i>q</i>-ROFS_<i>ft</i>R context to determine the criteria weights. Using the proposed distance measure, the final stage presents a hybrid <i>q</i>-ROFS_<i>ft</i>R-based technique for order of preference by similarity to ideal solution (TOPSIS) method to evaluate FWTTs with respect to multiple criteria and uncertain information. Further, the practicality of introduced methodology is demonstrated via a case study of FWTT selection, and the result indicates that “incineration” is the most suitable treatment technology followed by <i>anaerobic digestion</i>, <i>composting,</i> and <i>landfill</i>. Furthermore, the sensitivity and comparative analyses confirm the validity and stability of the proposed methodology. The present work offers valuable insights for selection of FWTT under uncertain environment, which also ensures the food security, economic growth, and environmental sustainability of a country.</p>