Vehicular Ad Hoc Networks (VANETs), a fast-evolving technology, improves safety and traffic management and acting as the foundation for intelligent transportation systems. Because VANET is linking more vehicles in a larger network, it is a tough challenge to provide bandwidth to all vehicles at all times. Consequently, the effectiveness of transportation is diminished. Cognitive Radio (CR) is a transformative communication paradigm that facilitates the efficient utilization and dynamic access to spectrum. Without knowledge of the signal's fundamental properties, it is a critical operation to sense the unused spectrum in conditions of low SNR and noise uncertainty. The proposed Threshold-based Fuzzy Deep Q-network (T-FuzzyDQN) is a new mathematical framework that has been developed to resolve the aforementioned challenges. This framework is designed to compute the triple threshold using the dynamic threshold factor. Clustering is implemented by the VANET environment vehicles and Roadside Units (RSUs) in accordance with vehicle density. The triple threshold mechanism is used to elect the cluster chief, who will be responsible for estimating the transmission in all clusters. The sensing findings are communicated to the RSU, which receives a fresh state and an incentive for sensing and obtaining the channel if it has not been in use. The RSU dynamically modifies the channel status in accordance with the present reward and condition after the spectrum sensing process. It strategically selects high-reward channels for efficient vehicle communication. This iterative procedure continues until congestion is effectively managed, ensuring reliable and uninterrupted transmission of emergency messages in the vehicular environment. Simulation findings demonstrate that, compared to existing works, the proposed T-FuzzyDQN yields superior results in spectrum management.