Explore data-driven modeling and topological data analysis (TDA) of self-organized multicellular architectures in this comprehensive lecture. Delve into the application of TDA and machine learning for automated classification of multicellular structures in cancer EMT and embryonic development. Learn about characterizing epithelial migration phases, computing persistent homology, and using unsupervised classification for topological features. Discover how this model-agnostic approach can provide quantitative insights into complex tissue topology emergence through spatiotemporal cell interactions. Examine simulation results, parameter sweeps, phase diagrams, and experimental findings. Gain understanding of limitations, ongoing work, and future directions in this field of study.