This study addresses high congestion in the spectrum poses a critical challenge, hindering efficient data transmission by proposing a novel hybrid deep learning-based spectrum sensing approach, contrasting it with the conventional Convolutional Neural Network (CNN) method. For analysis, a sample size of 20,000 was employed. The sample size was determined using the Kaggle website, dataset was split up into two categories: Deep Learning Classification Algorithm are included in Group 1, while Hybrid Deep Learning (HDL) are present in Group 2. The evaluation, conducted on a comprehensive dataset, showcases compelling results. This study, which used both the Deep Learning Classification Algorithm, highlighted how much more accurate Hybrid Deep Learning is at 94.2% when it comes to improve spectrum utilization compared with Convolutional Neural Networks at (87.8%). Through rigorous experimentation and analysis, this study demonstrates the superiority of the hybrid deep learning-based spectrum sensing technique. By outperforming traditional methods, our approach not only addresses the pressing issue of high congestion but also sets a new benchmark for spectrum utilization improvement in vehicular communications within heterogeneous networks. The hybrid deep learning model achieved significantly higher classification accuracy (94.2%) compared to the CNN model (87.8%). This superior accuracy was accompanied by notably lower false positive (4.1%) and false negative rates (6.7%) compared to the CNN-based approach, showcasing its precision in identifying both occupied and unoccupied frequency bands. This research marks a significant stride toward enhancing communication efficiency in dynamic environments, offering promising implications for future advancements in cognitive radio-enabled vehicular networks.