The rapid expansion of photovoltaic (PV) systems and the growing importance of sustainable energy utilization in Iran have highlighted the need for integrated fault diagnosis and modeling approaches to improve the efficiency, safety, and reliability of PV modules while reducing operating costs. Faults in PV systems may arise from environmental factors, mechanical and thermal stresses, improper transportation and storage, and installation errors. This study employed an analytical review approach based on a systematic search of scientific literature published between 2020 and 2026, focusing on fault diagnosis, environmental factors, and intelligent modeling in PV systems. The objective of this study was to review and analyze permanent and progressive faults in PV modules and investigate the associated environmental factors, performance impacts, fault diagnosis techniques, and intelligent modeling approaches for both fault categories. The effects of environmental factors, including solar irradiance, temperature, dust accumulation, humidity, precipitation, wind speed, and atmospheric pressure, on the electrical and thermal performance of PV modules were examined. Furthermore, PV module faults were classified into two main categories: permanent and progressive faults. The reviewed fault diagnosis techniques included visual inspection, current–voltage curve analysis, infrared thermography, and electroluminescence Limaging. The combined application of these techniques can improve fault detection accuracy. In addition, artificial intelligence, machine learning, and deep learning approaches can enhance fault detection, classification, localization, and prediction. Therefore, the implementation of early fault detection techniques and intelligent modeling approaches can contribute to improved system efficiency, reduced human error, and an increased share of solar energy in Iran's energy mix.