Research in machine learning models for Cognitive Radio–based Vehicular Ad Hoc Networks (CR-VANETs) focuses on applying intelligent learning algorithms to improve spectrum management, routing efficiency, and network security in dynamic vehicular environments. Recent studies employ supervised, unsupervised, and reinforcement learning models to enhance spectrum sensing accuracy, predict channel availability, and optimize spectrum handoff decisions under high mobility conditions. Deep learning frameworks, including convolutional and recurrent neural networks, are utilized for pattern recognition in spectrum occupancy and interference prediction. Federated and edge-assisted learning approaches are being integrated to enable decentralized, privacy-preserving, and low-latency model training across vehicles. Additionally, machine learning–driven trust and anomaly detection systems are developed to secure spectrum sharing and prevent malicious behaviors. These advancements collectively enhance adaptability, spectral efficiency, and communication reliability in next-generation CR-VANET architectures.