​The field of electrical engineering is witnessing rapid advancements driven by the significant progress in Artificial Intelligence (AI) technologies, which have become essential modern tools for enhancing the performance of electrical systems and raising their operational efficiency. Among these critical applications is the integration of AI in predicting electrical faults, a practice that significantly contributes to enhancing grid reliability and minimizing unexpected power outages. ​Traditional methods of electrical equipment maintenance often rely on periodic inspections or reactive interventions after a failure occurs, which can lead to equipment downtime and high maintenance costs. In contrast, AI technologies enable the analysis of operational equipment data—such as temperature, vibration, current, and voltage—to detect early indicators of potential malfunctions or future failures. ​Machine learning algorithms and artificial neural networks are utilized to analyze massive datasets collected from power transformers, electric 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 taking appropriate preventative actions before issues escalate. ​Furthermore, integrating AI with intelligent monitoring systems and the Internet of Things (IoT) provides continuous tracking of electrical equipment conditions and triggers alerts upon detecting abnormal operational changes. This integration helps optimize maintenance management, minimize downtime, extend equipment lifespan, and enhance worker safety while ensuring a stable power supply. ​Despite these advantages, applying AI in fault prediction faces several challenges. Key among these are the need for accurate and diverse datasets, the provision of suitable computing infrastructure, data protection against cyber risks, and the necessity of testing and validating models for accuracy before relying on them for critical operational decisions. Consequently, the role of the electrical engineer remains fundamental in interpreting results, reviewing recommendations, and making appropriate decisions. ​In conclusion, employing artificial intelligence in predicting electrical faults represents a vital step toward developing smarter, more efficient, and sustainable electrical power systems. These technologies are expected to further improve grid reliability and reduce operational and maintenance costs in the future, supporting industrial and technological development while aligning with the requirements of digital transformation in the energy sector. ​Al-Mustaqbal University — The First University in Iraq