International Journal of Scientific Research and Engineering Development

The introduction of the modernization of electrical utilities requires intelligent asset management strategies that can reduce failures, maximize performance, and improve the lifespan of assets. Conventional maintenance methods tend to be reactive or time-based, and this results in inefficiencies and high costs of operations. Artificial Intelligence (AI) has become one of the most effective tools to revolutionize the asset management process due to predictive analytics, real-time monitoring, and automated diagnostics. The paper looks into the implementation of AI-driven analytics in smart asset management of electrical utilities. The different AI models which include machine learning, deep learning in visual inspections, and natural language processing in the document analysis are discussed on theirapplication in assets health prediction, anomaly detection, and optimization of maintenance. Through reallife examples and case studies, we explain the effectiveness of the AI models LSTM, XGBoost, and CNN in predicting equipment breakdowns and improving the decision process. The issues concerning data quality, model transparency, and cybersecurity are addressed, and the future directions such as explainable AI and digital twins are discussed. The piece has brought a systematic approach to deploy AI-based analytics into utility asset management systems, which will enhance reliability, resiliency, and costeffectiveness throughout the power industry.