With the emerging and steady reliance on solar energy as a viable substitute for traditional fuel-based energy, maintenance is becoming an essential issue for both generators and users. Defects frequently cause the installed solar panel’s efficiency in broader environmental areas to decline. Therefore, identifying these defects in the solar panel images is one of the most crucial responsibilities for improving the output effectiveness of solar panels. The use of artificial intelligence technologies like neural networks and others to identify solar panel deficiencies and determine their lifespan is made possible by electroluminescence technology. The deep learning technology has surfaced in recent years to provide new opportunities for learning accuracy and to extract relevant information from many applications, especially those that primarily rely on images. This proposal examines the most significant studies that have recently used deep learning to analyze defects obtained in the solar panel. The datasets of the modules are collected and preprocessed by region-based histogram approximation (RHA). The data’s extracted from images by Grey Level Co-occurrence Matrix (GLCM). The processed images are split for training and testing. In training, Random Forest (RF) is employed for detection of faults based on their severity in solar panels, and defects are classified by Long Short-Term Memory (LSTM) algorithm. These algorithms use MobileNet model to categorize those defects in the solar PV system. In this way, the performance analysis is obtained with the accuracy of the system.