The recent advancements in spatio-temporal modeling and predictive learning have significantly enhanced the efficiency and accuracy of various urban and network-related predictions. Researchers are increasingly focusing on developing unified models that can handle diverse data types and temporal dynamics, addressing the complexities of urban environments and network traffic. These models leverage novel architectures such as transformers and convolutional networks, which are being optimized for better computational efficiency and performance. Notably, the integration of graph-based and grid-based data representations is becoming a standard approach, enabling more comprehensive and scalable solutions. Additionally, the incorporation of adaptive learning techniques, such as Principal Component Analysis (PCA) embeddings, is showing promise in improving model generalization and robustness across different scenarios and cities. The field is also witnessing a shift towards explainable AI, particularly in multi-agent reinforcement learning applications, where transparency and trust are critical for practical deployment. These developments collectively point towards a future where predictive models are not only more accurate and efficient but also more adaptable and interpretable.