This chapter introduces a novel approach leveraging Artificial Intelligence (AI) within Vehicle-to-Vehicle (V2V) communication protocols. The research focuses on three key areas: intelligent resource allocation, predictive maintenance, and enhanced security. Specifically, it explores how AI-powered methods can improve channel selection, power allocation, and bandwidth utilization. This leads to more efficient routing of information within the network, effectively mitigating communication failures and ensuring system reliability while reducing downtime. Routing refers to the process of determining the best path for data to travel across a network from a source to a destination. This research addresses security threats and methods for maintaining privacy and data integrity. Optimization, a key aspect of resource allocation, aims to find the best solution among a set of possible options, maximizing desired outcomes like efficiency and minimizing undesirable factors like latency. Machine Learning (ML), a subset of AI, is employed to enable systems to learn from data without explicit programming, improving their performance on a specific task over time. The ML techniques utilized in this chapter are for predictive maintenance and intelligent resource allocation. This research work contributes to the development of seamless and connected mobility in urban transportation. Experimental results demonstrate the effectiveness of the proposed AI-driven enhancements to the V2V communication ecosystem.