Intelligent transportation systems offer a promising avenue for enhancing road safety by facilitating communication between vehicles and infrastructure. This communication can significantly reduce road fatalities. Addressing congestion resulting from wireless communication between vehicles and infrastructure is a critical aspect of road safety. This paper focuses on effectively controlling this congestion while ensuring constant awareness in vehicles. The proposed technique involves determining an optimal transmission rate based on the current channel conditions, striking a balance between message awareness and congestion. This is achieved using a reinforcement learning and Markov decision process-based machine learning algorithm. Simulation results demonstrate the superiority of applying machine learning algorithms over traditional congestion control strategies. Additionally, future studies should consider incorporating other characteristics such as packet delay and packet loss for a comprehensive analysis.