Despite the critical role played by power grids in smart cities, traditional systems often detect issues too late leading to costly outages and safety risks. This paper presents a novel approach by leveraging graph-based digital twins created with the help of Microsoft Azure IoT TwinMaker to enable an always-on view of real-time power grids. For one to derive a virtual model of this grid that reflects what happens in its counterpart in the physical plane, digital twins have this application. Simulation of the grid’s behavior helps reveal existing problems to operators without affecting the infrastructure. The system incorporates advanced machine learning techniques, including a genetic algorithm, which can predict failures in advance by taking realtime and previously recorded data from the digital twin. This approach is proactive, thereby preventing disruptions and operational risks. It ensures scalability and efficient processing of large datasets when running machine learning models in the cloud on Microsoft Azure, thus adapting the system to the complex needs of modern power grids in smart cities. Using machine learning in the digital twin rather than in the physical grid minimizes real-world testing and avoids unanticipated downtime, thereby saving costs. This approach not only enhances the accuracy of fault detection but also improves the lasting ability and steadiness of the grid. The solution aims to support the rising energy demands of smart cities by offering a scalable, cost-effective, and reliable way of managing power grids.