The field of reduced order modeling and system identification is witnessing significant developments, driven by the increasing need for efficient and accurate modeling of complex systems. Researchers are exploring innovative methods to improve the accuracy and interpretability of reduced order models, including the use of machine learning techniques and data-driven approaches. A key direction in this area is the development of non-intrusive reduced order modeling techniques, which can handle high-dimensional systems and provide interpretable results. Another important trend is the application of system identification methods to real-world problems, such as modeling the dynamics of buck converters and predicting the behavior of robot appendages interacting with granular materials. Notable papers in this area include the development of a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models using Multi-Input Operators Network, and the application of Kolmogorov-Arnold Networks to model and analyze the dynamics of a buck converter system. The use of data-driven moment matching methods for parametric reduced-order models is also a promising direction, as demonstrated by its application to linear and nonlinear parametric systems.