The evaluation of autonomous urban freight logistics (UFL) solutions is crucial due to the increasing need for efficient and sustainable transportation systems in urban areas. Optimizing UFL possibilities becomes essential for developing smart city projects as cities expand and the need for reliable logistical services increases. This study introduces an artificial intelligence (AI)-driven framework for group decision-making in UFL evaluation. It develops single-valued neutrosophic (SVN) Copula-Dombi averaging and geometric operators that act as AI reasoning tools, capable of handling uncertainty, contradiction, and indeterminacy beyond classical models. To improve reliability and agreement among decision-makers, the study also presents a consensus-based SVN Copula-Dombi multiple triangles scenarios (MUTRISS) decision support model. The model is tested on a real case in India. It evaluates drones, electric light commercial vehicles (e-LCVs), autonomous e-LCVs, and droids as UFL solutions. Twelve criteria are used, and their weights are set with an optimization model. Results show drones rank first with a score of 0.6055, followed by droids at 0.6033. The study also checks robustness through comparison and sensitivity tests. The study supports smart city logistics and sustainable urban development by assisting logistics managers and city authorities in selecting appropriate autonomous UFL systems.