Vehicular ad hoc networks (VANETs) have become a significant element in smart transportation systems, ensuring better safety and efficiency in vehicular communication. One major challenge for the secured data flow in VANETs is that the topology of VANETs is dynamic, with a high mobility of vehicles. Authentication is key in making sure that the communication is secure, building trust, warding against several attacks, and protecting the privacy of the users. Many existing authentication approaches frequently pose some major challenges, including considerable computing overhead, latency, and scalability. To do away with these problems, we propose a novel ensemble model based on bilinear mapping and utilizing Bidirectional Long Short-Term Memory (BLSTM) networks and stochastic gradient descent (SGD) optimization. The accuracy of the computing process is drastically improved with the introduction of our proposed model in the authentication procedure, simultaneously reducing the complexity. It is quite apparent from the findings of experiments that our method does better with streamlined data processing and optimized calculation technique than traditional ways and provides a much stronger and trustworthy solution for VPNs of VANETs.