Recent research in opportunistic routing for Vehicular Ad Hoc Networks focuses on improving data delivery reliability and efficiency in highly dynamic and unpredictable vehicular environments. These studies emphasize adaptive routing mechanisms that exploit real-time network conditions, vehicle mobility patterns, and link quality to opportunistically select the best next-hop nodes. Machine learning and deep reinforcement learning techniques are increasingly being applied to predict connectivity and optimize relay selection decisions. Trust-aware and secure opportunistic routing frameworks are also being developed to mitigate malicious behaviors and ensure data integrity during transmission. Additionally, hybrid models integrating edge computing, fog nodes, and blockchain are enhancing scalability and reducing latency in data dissemination. Overall, these intelligent opportunistic routing solutions aim to achieve high throughput, minimal packet loss, and robust performance in complex vehicular communication scenarios.