By conducting detection of faults and condition monitoring, solar module efficiency and dependability can be raised. The limitations and challenges of diagnosing solar module malfunctions are examined in this study. The many issues connected to solar module failure have been well discussed. A monitoring tool that uses the thermography and computers with intelligence to discover various issues with photovoltaic cells while also filtering out trivial anomalies is developed after reviewing pertinent studies. Given the increasing expansion of solar energy, the photovoltaic (or PV) component detects faults plays a critical role in understanding how to increase the dependability of the total solar power system and detecting the fault type any time a system issue emerges. As a result, this paper presented a hybrid strategy employing the chaotic synchronisation detection approach (CSDM) and a convolutional neural network (CNN) for investigating PV module fault detection. Problem identification and treatment in PV systems have become equally important with the global photovoltaic (PV) market's explosive growth. Identifying issues early will improve a solar system's efficiency, measurement outcomes, and lifespan. The plant's production of electricity will suffer greatly if these PV flaws are not quickly identified and fixed. Both on-site and online monitoring and identification of defects are possible. In addition, a few flaws including ground faults, arc faults, line-to-line errors, and points of ignition may pose a fire hazard. Researchers have recently proposed several methods for diagnosing and detecting PV issues. In-depth discussions of significant photovoltaic defects are provided in this work. The distinctions, benefits, and drawbacks of the various approaches for finding faults in PV systems that have been suggested in the available research are also highlighted. A brief discussion of various solar cell modelling models is also provided.