​The world is witnessing rapid advancement in the field of electrical engineering as a result of significant progress in artificial intelligence (AI) technologies, which have become modern tools used to enhance the performance and operational efficiency of electrical systems. Applying AI to predict electrical faults is a critical application that contributes to boosting grid reliability and minimizing sudden power outages. ​Traditional maintenance methods for electrical equipment often rely on periodic inspections or reactive intervention after a fault occurs—approaches that can lead to equipment downtime and elevated maintenance costs. In contrast, AI techniques enable the analysis of operational data, such as temperature, vibration, current, and voltage, to detect early indicators that may signal an anomaly or a potential future failure. ​Machine learning algorithms and artificial neural networks are employed to analyze large volumes of data collected from electrical transformers, motors, and transmission lines. By training these systems on historical data, they can recognize abnormal patterns and estimate the probability of faults, thereby assisting engineers in implementing appropriate preventive measures before issues escalate. ​Furthermore, integrating AI with smart monitoring systems and the Internet of Things (IoT) provides continuous condition monitoring of electrical equipment, generating alerts when abnormal changes in operating conditions are detected. This integration helps optimize maintenance management, reduce downtime, extend equipment lifespan, enhance worker safety, and ensure a stable power supply. ​Despite these advantages, deploying AI for fault prediction faces several challenges. Most notable among these are the need for accurate and diverse datasets, suitable computing infrastructure, data protection against cybersecurity threats, and the thorough testing and validation of models before relying on them for critical operational decisions. Consequently, the role of the electrical engineer remains essential for interpreting results, reviewing recommendations, and making informed decisions. ​In conclusion, the deployment of artificial intelligence in predicting electrical faults represents a major step toward developing smarter, more efficient, and sustainable power systems. In the future, these technologies are expected to enhance grid reliability and lower operation and maintenance costs, thereby supporting industrial and technological development while keeping pace with the digital transformation of the energy sector. ​Al-Mustaqbal University – The First University in Iraq.