This study investigates spatial distribution and chemical elemental composition screening in soils in Rome (Italy) using X-ray fluorescence analysis. Fifty-nine soil samples were collected from various locations within the urban areas of the Rome municipality and were analyzed for 19 elements. Multivariate statistical techniques, including nonlinear mapping, principal component analysis, and hierarchical cluster analysis, were employed to identify clusters of similar soil samples and their spatial distribution and to try to obtain environmental quality information. The soil sample clusters result from natural geological processes and anthropogenic activities on soil contamination patterns. Spatial clustering using the k-means algorithm further identified six distinct clusters, each with specific geographical distributions and elemental characteristics. Hence, the findings underscore the importance of targeted soil assessments to ensure the sustainable use of land resources in urban areas.