The field of predictive modeling and spatiotemporal analysis is experiencing significant growth, driven by the increasing availability of data and advancements in machine learning techniques. Researchers are exploring new approaches to improve the accuracy and efficiency of predictive models, including the use of graph neural networks, physics-informed neural networks, and multimodal knowledge graphs. These innovations are enabling more effective forecasting and analysis of complex phenomena, such as satellite trajectories, power outages, and disease outbreaks. Notable papers in this area include: A Data-Driven Approach to Estimate LEO Orbit Capacity Models, which proposes a novel approach to modeling satellite orbits using sparse identification of nonlinear dynamics and long short-term memory recurrent neural networks. Enhancing Spatiotemporal Networks with xLSTM, which introduces a lightweight and efficient spatiotemporal network for cellular traffic forecasting. A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting, which demonstrates the superiority of deep learning models in predicting influenza outbreaks. BuildSTG, which proposes a multi-building energy load forecasting method using spatio-temporal graph neural networks. Physics-Informed EvolveGCN, which leverages dynamic graphs and physics-constrained loss functions to forecast the evolution of inter-agent relationships in multi-agent systems. HGCN(O), which introduces a self-tuning toolkit for event sequence prediction using graph convolutional network models. GeoOutageKG, which proposes a multimodal geospatiotemporal knowledge graph for multiresolution power outage analysis.