Autonomous vehicles (AVs) are transforming modern transportation, but their reliability and safety are significantly challenged by harsh weather conditions such as heavy rain, fog, and snow. These environmental factors impair the performance of cameras, LiDAR, and radar, leading to reduced situational awareness and increased accident risks. Conventional cloud-based AI systems introduce communication delays, making them unsuitable for the rapid decision-making required in real-time autonomous navigation. This paper presents a novel Edge AI-driven real-time decision-making framework designed to enhance AV responsiveness under adverse weather conditions. The proposed approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for improved perception, alongside reinforcement learning (RL)-based strategies to optimize vehicle control in uncertain environments. By processing data at the network edge, this system significantly reduces decision latency while improving AV adaptability. The framework is evaluated using simulated driving scenarios in CARLA and real-world data from the Waymo Open Dataset, covering diverse weather conditions. Experimental results indicate that the proposed model achieves a 40% reduction in processing time and a 25% enhancement in perception accuracy compared to conventional cloud-based systems. These findings highlight the potential of Edge AI in improving AV autonomy, safety, and efficiency, paving the way for more reliable self-driving technology in challenging real-world environments.