During recent days, solar energy has developed as a promising alternative to conventional energy resources. However, dust accumulation and shading on solar panels can significantly affect on efficiency of the panel. Hence, periodic cleaning is very important for maintaining optimal performance of solar photovoltaic systems. Again, under certain conditions, manual cleaning can often be difficult and auto cleaning is the preferred solution of that problem. This paper introduces an innovative solar panel condition monitoring system to detect and classify dust and shading levels using a multiagent system and deep neural network. The proposed system consists of three specialized agents, each focuses on specific function. The first agent assesses the panel’s condition as healthy, dusty, or shaded using K-nearest neighbor classification. The second agent evaluates the level of dust, while the third agent determines the shading level using a deep neural network. By allocating tasks among these specialized agents, the system provides an efficient and scalable approach for real-time monitoring and maintenance of solar panels. Experimental results from a 5 KWp solar panel indicate that the proposed system effectively classifies the condition of solar panels, offering a practical solution for ongoing monitoring and maintenance, thus enhancing energy output and extending the operational lifespan of the panels.