Metal nanoparticles (NPs) exhibit properties significantly differing from their bulk counterparts due to their size, shape, surface, and dynamical features. A fundamental understanding of the properties of metal NPs is of special interest due to their importance in a broad range of applications, from industrial catalysis to medicine, to name a few. However, this requires obtaining detailed insight into their atomic structure and dynamics which are typically not easy to attain. Gold (Au) NPs are a relevant example. Being the most stable among transition metals, bulk Au is often considered an inert catalyst. On the other hand, Au nanoparticles (Au NPs) are in comparison surprisingly active and effective catalysts, capable, e.g., of oxidizing CO into CO 2 in atmospheric conditions, and of catalyzing various other oxidative transformations. Notably, Au NPs also feature surface plasmon resonance (SPR) and several other distinct physical and chemical attributes, somehow directly related to the shape and features of their surface, which make them interesting candidates for sensor devices and biomedical applications. Metals in general are known to assume a non-trivial dynamic behavior, where atoms enter a dynamic steady state and can move in the lattice, well below their melting temperature. Such atomic mobility allows dynamic transformations of the material's surface and reconstruction of specific atomic environments. This is even more relevant in the case of metal NPs, due to a well-known (albeit not general) dependence of their melting temperature with size, meaning that smaller NPs may exhibit significant surface atomic mobility even at relatively low temperature. Understanding the dynamic properties of the atomic sites that populate the surface of Au NPs and how these evolve and change in time in relevant conditions is thus of utmost importance. For example, it may facilitate the rational design of more effective NP-based heterogeneous catalysis strategies. Recently experimental investigation techniques have been developed increasing the resolution up to single particles or even individual atoms. For example, high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) experiments of supported Au NPs provided direct evidence that atoms move in the NPs at finite temperatures. However, unraveling such atomic motion and obtaining quantitative insight about it remains non-trivial for a series of fundamental reasons, including, e.g., the structural dispersion and variable distribution of atomic surface sites, which details get considerably smoothed out in ensemble-averaged characterizations, or the fact that experimentally-reconstructed NP models do not contain information concerning the identity of the individual atoms (so that following their dynamics over time is impractical). Widely used for studying metals and metal clusters, computational modeling holds a considerable potential in this sense. Molecular simulations have been used to study Au NPs with considerable success. The intrinsic ability of atomistic simulations to capture individual atomic motions and track them over time is particularly advantageous for the reconstruction of the internal atomic dynamics of NPs. Machine learning approaches were recently found very useful to analyze molecular dynamics (MD) trajectories of various types of complex molecular systems, including Au NPs. In particular, unsupervised clustering and advanced statistical analyses of high-dimensional smooth overlap of atomic positions (SOAP) data extracted from MD simulations recently allowed to reconstruct of the structural/dynamical complexity of a variety of molecular materials/systems and to build robust data-driven metrics useful for their classification. Here we report a general data-driven approach that allows resolving at atomistic-resolution the complex dynamics within metal nanoparticles of different shapes and sizes in relevant conditions. We focus on gold NPs as a relevant example, but the approach is in principle versatile and applicable to a variety of metal NPs. We simulate various types of Au NPs (i.e., icosahedral, decahedral, octahedral) on relevant spatiotemporal scales. A combination of bottom-up and top-down SOAP-based data-driven analyses reveal the atomic environments (AEs) that statistically populate Au NPs along the MD simulations at various temperatures, permitting tracking the native and non-native AEs -that is, AEs typical of other types of NPs -that continuously emerge/resorb on the NPs. This allows us to obtain precious information on their emergence, annihilation, survival lifetime, and dynamic interconversion, and a unique insight into how such innate atomic dynamics shape the "statistical identity" of the NPs in given conditions.