<p>We investigate whether artificial intelligence can reproduce and organize known structures of the Standard Model of particle physics using experimental data and with minimal theoretical inputs. By applying unsupervised machine learning techniques — including data dimensionality reduction and clustering algorithms — to intrinsic particle properties and decay modes, we show that key organizational features of particle physics, such as the relative strength of different interactions and the difference between baryons and mesons, become evident in the resulting data representations. We also identify conserved quantities such as baryon number, strangeness and charm as well as the structure of isospin and the Eightfold Way multiplets. Our analysis then reveals that clustering can separate particles by interaction, flavor symmetries as well as quantum numbers. Additionally, we observe patterns consistent with Regge trajectories in baryon excitations. Our results show that machine learning can highlight known key structures of the Standard Model from data, suggesting a promising path toward data-supported discovery in fundamental physics.</p>