Prepared by: Engineer Rand Maan Ahmed Institution: Department of Electrical Engineering Technologies, College of Engineering Technologies, Al-Mustaqbal University The sustainability of electrical power networks and industrial equipment represents the cornerstone of stability in modern technological systems. With the continuous expansion of electrical loads, traditional methods of periodic maintenance and fault detection have become insufficient to prevent sudden outages. This paper/article aims to highlight strategies for deploying artificial intelligence (AI) and machine learning (ML) techniques to proactively predict and accurately diagnose electrical faults in advanced systems, showcasing the research integration between electrical engineering technologies and computer engineering technologies. 1. Introduction Electrical networks and equipment (such as substations, transformers, and cast-iron motors) are subjected to thermal, electrical, and mechanical stresses that over time lead to partial discharges or variations in voltage and current levels. Relying on traditional protection devices ensures system disconnection only after a fault occurs, whereas AI techniques enable a transition from "remedial or periodic maintenance" to "Predictive Maintenance," allowing anomalous behaviors to be detected before they escalate. 2. Methodology and Adopted Machine Learning Techniques The intelligent diagnostic system relies on collecting real-time data through sensors distributed across the network, followed by signal processing and analysis using the MATLAB/Simulink environment to apply the following machine learning algorithms: Support Vector Machines (SVM): To accurately distinguish between normal operating conditions and various types of faults. Artificial Neural Networks (ANN): To recognize complex and non-linear patterns in current and voltage signals. Decision Trees and Random Forest: To classify faults and extract the technical rules causing the malfunction. 3. Academic and Research Integration Between Specializations The significance of this approach lies in highlighting the integrative synergy between different engineering disciplines within the university: Department of Electrical Engineering Technologies: Provides the physical engineering understanding of electrical phenomena, load capacity analysis, and the interpretation of fault signals and waveform characteristics. Department of Computer Engineering Technologies: Handles algorithm design, smart model training, and the construction of advanced programming interfaces to manage Big Data. 4. Conclusions and Recommendations Deploying artificial intelligence demonstrates high efficiency in elevating electrical system reliability and reducing operational costs resulting from unexpected downtime. We recommend expanding fault prediction research to integrate smart measurement devices with the Industrial Internet of Things (IIoT) concept, alongside providing advanced laboratory simulation environments for students of the Electrical Engineering Technologies Department to apply these models in the field. Al-Mustaqbal University, the first university in Iraq.