Many persistent organic pollutants (POPs) in human milk present environmental and health concerns, particularly for vulnerable populations such as breastfed infants. This study applied a data exploration dimensionality reduction workflow using Uniform Manifold Approximation and Projection (UMAP) for POP measurements in human milk. This approach focuses on identifying and comparing the compositional patterns-or 'fingerprints'-of POPs rather than just measuring their concentrations. The UMAP approach revealed detailed variations in POP fingerprints that were not detectable with traditional univariate approaches. UMAP approach also improves upon other multivariate approaches, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA). Unlike previous studies focusing solely on concentration differences, UMAP identified distinct regional and economic POP fingerprints. Lower-income countries showed POP fingerprints dominated by DDT-related compounds, while higher-income regions showed distinct fingerprints with greater contributions from PCBs and other legacy pollutants. Temporal analysis captured shifts in POP fingerprints after 2001, corresponding with the expansion from initial focus on PCDD, PCDF, and PCB to the broader group of the original 12 POPs listed under the Stockholm Convention. These results demonstrate how dimensionality reduction techniques, particularly UMAP, can distinguish compositional POP fingerprints across geospatial, temporal, and economic factors, providing a comparative framework for understanding global exposure patterns.