The integration of advanced techniques in 3D modeling, Gaussian Splatting, and graph-based machine learning is driving significant progress across multiple research areas. In 3D modeling, the shift towards implicit neural representations and adaptive Gaussian Splatting is enhancing efficiency and accuracy, particularly in dynamic and real-time applications. Notable innovations include training-free frameworks, multi-modal data integration, and interactive editing tools, which are making 3D models more versatile and user-friendly. In graph-based machine learning, the focus on feature-rich approaches and dynamic graph contrastive learning is improving clustering and representation learning, with applications ranging from social network analysis to knowledge graph embeddings. Key advancements like hyperbolic hypergraph neural networks and Lorentzian residual neural networks are pushing the boundaries of graph representation and link prediction tasks. Overall, these developments are paving the way for more intelligent, efficient, and practical solutions in both 3D modeling and graph-based machine learning, with implications for a wide range of real-world applications.