This study presents an AI-driven fault diagnosis system for solar panel units, leveraging an upgraded You Only Look Once (YOLO) model named YOLOv11 model. The proposed system improves real-time fault detection accuracy while maintaining high computational efficiency. The growing use of solar energy has brought attention to the need for efficient maintenance of photovoltaic (PV) systems. Solar panel faults, such as cracks, hotspots, and dirt accumulation, can significantly reduce energy output and system efficiency. Manual inspection methods are labor-intensive and prone to human error, so an automated approach is required. Using a dataset of annotated images, the improved YOLO model is refined to detect and categorize a variety of solar panel faults. To enhance model robustness, advanced image pre-processing techniques and data augmentation are used. The system also incorporates a post-detection analysis module that offers actionable insights for maintenance planning, which guarantees early fault detection, minimizes downtime, and maximizes PV system performance. The study shows that the YOLOv11 model is effective in achieving high precision and recall rates, establishing its potential for scalable deployment in solar energy systems.