The proliferation of vehicle-to-everything (V2X) communication systems with diverse quality of service (QoS) requirements has created an increasingly complex heterogeneous wireless environment where vehicles must intelligently select among multiple radio access technologies (RATs) to meet these diverse and stringent QoS requirements. In this paper, we address the critical challenge of optimal RAT selection in multi-RAT vehicle to infrastructure (V2I) communication. We propose an intelligent adaptive RAT selection algorithm that combines reinforcement learning with predictive mobility modeling to enable real-time decision making under uncertainty and incomplete information scenarios with a view to simultaneously optimizing latency and reliability while reducing the number of handovers under rapidly changing vehicular network conditions. To validate our approach, we conduct extensive network simulations and the results demonstrate significant improvements compared to state-of-the-art RAT selection method: up to 63.0% reduction in number of handovers, 1.8 and 2.7% improvement in overall vehicle reliability, 12.1 and 6.2% reduction in delay experience by vehicles at low and high number of vehicles, respectively.