Detecting, classifying, and localizing electrical faults in the transmission lines of solar panels operating based on the photovoltaic effect principle are critical for ensuring the efficiency, safety, and longevity of photovoltaic (PV) systems. While solar panels are built for long-term use, they can still be prone to faults caused by environmental factors, wear and tear, or manufacturing defects. Timely detection and mitigation of these faults are essential for maintaining system performance and preventing hazardous incidents such as electrical fires. Recent studies in this field have increasingly highlighted the significance of early fault detection in enhancing the operational efficiency of solar panel systems. A wide range of techniques, from traditional approaches such as visual inspections and infrared thermography (IRT) to advanced methods employing machine learning (ML) and artificial intelligence (AI), have been developed and continue to evolve. This study addresses methods for detecting electrical faults, techniques for classifying electrical faults, strategies for localizing fault locations, preventive measures, case studies, and future directions in solar panel maintenance.