Characterizing the complex atomic dynamics present on the surface of metal NPs in relevant regimes is fundamental for understanding their properties. But this is typically hard, both experimentally and computationally. Here we present a concerted approach where such a detailed characterization is attained via a combination of a bottom-up and top-down data-driven analyses of steady state MD trajectories of various types of Au NPs. In the first step, the bottom-up ML analysis detects in a purely data-driven way the main atomic environments (AEs) that populate an NP in MD steady state based on the levels of order/disorder and the structural similarity/dissimilarity between them as captured by high-dimensional SOAP data extracted from the MD trajectories, and further dimensionality reduction and unsupervised clustering (Figures and). Tracking the individual atoms along the MD and classifying them based on the detected SOAP environments allows resolving the complex atomic dynamic that is present on the NP surface at different temperatures (see transition matrices in Figures and). In particular, this allowed us to identify in a data-driven unbiased way all the AEs that populate the NPs in the MD local equilibrium and to understand where these come from and where they go in terms of interconversion into other AEs. On a second step, a top-down data-driven classification based on the similarity/distance between/from the SOAP spectra of each atom at each sampled step of the steady state MD trajectories and the SOAP spectra characteristic of a variety of different Au NPs contained in a SOAP spectra dictionary. Such a SOAP dictionary contains the SOAP spectra of all AEs proper of ideal, e.g., icosahedral, decahedral, and truncated-octahedral Au NPs, which allows us to learn which ones of the AEs appearing on the surface of a given simulated NP in MD steady state are "native" of that type of NP, and which ones are "non-native" (i.e., typical of ideal NPs of a different shape). We repeat such analysis on three types of different-shape Au NPs (Figures i.e., Ih 309 , Dh 348 and To 309 ). Notably, such analysis allows us not only to estimate the "statistical equivalent identity" of the various NPs -namely, what do the NPs look-like in relevant dynamic regimes (histograms of Figures ) -, but also obtain relevant dynamic information as on the average lifetime and interconnection rates of all visited atomic (native and non-native) environments present on their surface (transition matrices in Figures ). While the dictionary of AEs presented herein is based on the analyzed set of Au NPs, it can be easily expanded to include more particle geometries. Nevertheless, the provided analysis already shows the generality of such an approach, i.e., it can be applied to any kind of metal NP.