The field of predictive modeling is witnessing significant advancements in tackling complex systems, with a notable emphasis on developing innovative frameworks that can accurately forecast events and patterns. Recent research efforts have focused on creating transferable and generalizable models that can be applied across different networks and domains, such as aviation, public health, and transportation systems. These models leverage cutting-edge techniques, including graph neural networks, LSTM networks, and XGBoost classifiers, to capture intricate relationships and temporal dependencies within complex systems. The ultimate goal of these endeavors is to provide actionable insights that can inform decision-making and mitigate potential disruptions. Noteworthy papers include: Queue up for takeoff, which introduces a novel approach combining queue theory with an attention model to predict flight delays with high accuracy. Forecasting Coccidioidomycosis, which develops a graph neural network model to predict Valley Fever incidence in Arizona, providing valuable insights into environmental drivers of disease transmission.