Free Space Optical (FSO) communication systems offer high bandwidth and immunity to electromagnetic interference, making them attractive for next-generation wireless communications. However, they are highly susceptible to atmospheric conditions such as fog, dust, and precipitation, which can severely degrade signal quality. In this paper, we propose a novel hybrid artificial intelligence framework that enhances the robustness and adaptability of FSO systems. Our approach integrates deep computer vision and reinforcement learning (RL) techniques to dynamically respond to changing weather conditions. First, we utilize a convolutional neural network to classify environmental weather conditions from captured images. This image-based classification enables precise identification of channel impairments, providing crucial state information for decision-making. Next, we employ a Q-learning agent that receives the classified weather condition and selects optimal actions to adjust the transmission power and data rate. The goal is to maintain signal quality by maximizing the Signal-to-Noise Ratio, minimizing Bit Error Rate, and reducing power consumption. Extensive experiments were conducted using a custom dataset of weather images and a simulated FSO channel model. Results demonstrate that the proposed system can accurately classify seven distinct weather types and significantly improve FSO performance through intelligent control.

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