Abstract— The process of developing in intelligent shadow detection system for solar panels using stand-alone cells and visible and infrared (RGB/IR) imaging techniques The experiment relied an reproducibility intelligence algorithms neural networks and hybrid algorithms such as ANFIS and SCFNN to analyze images and predict the effect of shadows on energy production The methodology was implemented using MATLAB and the modeling of shadow effects in maximum power point tracking (MPPT) systems were demonstrated. It has been proven that uniform shadow distribution on panel surfaces reduces energy waste and increases production efficiency. The study presents a model for reconfiguring a photovoltaic array using image analysis to track shadow movement during operation. It demonstrates the development of an intelligent system that uses imaging techniques and algorithmic analysis to improve the performance of solar panels under the influence of shadow and dust. Field experiments indicate conventional methods. It appears that dust accumulation significantly reduced the panel’s efficiency. Three levels of dust density were examined, and their visual data were later analyzed through artificial neural networks (ANNs). Although the outcomes were not entirely consistent, the statistical results suggest a clear and meaningful relationship. This indicates that combining image analysis with AI enhances the system’s ability to track the optimal power point and improves the overall efficiency of the photovoltaic system.