Recent research on congestion prediction-based emergency vehicle dynamic route discovery in Vehicular Ad Hoc Networks (VANETs) focuses on leveraging real-time traffic analytics, artificial intelligence, and predictive modeling to ensure rapid and congestion-free navigation for emergency vehicles. Advanced frameworks integrate machine learning models such as LSTM, CNN, and fuzzy logic controllers to forecast road congestion levels and dynamically update routes based on vehicular density and signal timing. Hybrid communication models using both RF and visible light communication (VLC) improve message delivery reliability under heavy traffic conditions. Researchers have also proposed multi-agent and reinforcement learning algorithms that enable vehicles to collaboratively share traffic data and optimize path selection in real time. Furthermore, edge computing and cloud-assisted architectures are being employed to process congestion data locally, reducing latency and ensuring faster decision-making for emergency route planning. These approaches collectively enhance the responsiveness, safety, and efficiency of emergency vehicle navigation in complex urban environments.