<p>Vehicular Ad Hoc Networks (VANETs) are important in intelligent transportation systems, in which the mobility of nodes and the dynamic changes in topology are critical to both reliable and low-latency communication. The presented paper suggests a Quantum-Swarm Cognitive Routing Framework with Neuro-Fuzzy Reinforced Meta-Optimization (QSCR-NF-RM) designed to combine quantum context encoding, swarm-based exploration, neuro-fuzzy reinforcement learning and entropy-based meta-optimization to design adaptive and ultra-reliable vehicular routing. The framework is tested on a hybrid SUMO-OMNeT +Qiskit simulation platform with different vehicle densities (20–200 nodes), mobility models, and traffic loads and is compared to AODV, RL-VANET, Q-AntNet and DRLIQ. The experimental findings indicate that QSCR-NF-RM can achieve a packet delivery ratio of up to 97.81% or an improvement of 15–20% over AODV and 4–6% over learning-based baselines as well as lowering end-to-end delay by about 34% compared with AODV in a higher-mobility environment. Also, the offered framework minimizes the average per-node energy use by approximately 41% and increases network throughput to up to 23% in contrast to standard and learning-based routing protocols. These findings affirm the success of QSCR-NF-RM in providing stable, energy efficient and situational routing services to next generation vehicular networks.</p>