The recent advancements in the research area have primarily focused on leveraging AI and machine learning techniques to enhance operational efficiency, fault detection, and infrastructure management across various sectors. A significant trend is the integration of hybrid models, combining both model-based and data-based approaches, to address complex challenges in fault diagnosis and system maintenance. These hybrid methods are proving to be more effective, especially in scenarios where data is scarce or systems are highly heterogeneous. Additionally, the use of Vision Transformers (ViTs) and other Transformer-based models is gaining traction for their ability to handle complex visual data and improve the accuracy of tasks such as power plant detection and indoor pathloss radio map prediction. Another notable development is the application of AI in proactive network maintenance, exemplified by systems like CableMon, which aim to enhance the reliability of cable broadband networks through advanced data analysis. Overall, the field is moving towards more intelligent, data-driven solutions that promise to revolutionize traditional methods of operation and maintenance.