The goal of this research is to create an intelligent dust detection system for solar panels in order to improve their energy performance in the face of adverse environmental circumstances, particularly dust collection. The suggested system uses a camera module attached to a Raspberry Pi to take real-time photos of a 200×200 mm glass surface, imitating a solar panel. These photos are evaluated using a convolutional neural network (CNN) that can classify the surface's cleanliness. The technology detects dust buildup and sends out preventive maintenance reminders. With a classification accuracy of 94.53%, the model assures consistent dirt detection, assisting in maintaining optimal energy output, improving operating efficiency, and lowering maintenance expenses. From a practical aspect, the created solution provides an automated, cost-effective, and simply deployed instrument for monitoring the cleanliness of photovoltaic installations, particularly in locations prone to dust accumulation. This study is unique in that it uses an artificial intelligence-based approach to integrate dust detection into a predictive maintenance strategy, with one or more sensors placed near to the solar panels depending on the size of the installation. The findings emphasize the importance and promise of AI in the intelligent and sustainable management of renewable energy systems.