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Abstract. Lead (Pb) and its isotopes are powerful tools for studying the pathways of Pb pollution from land to sea and, simultaneously, investigating biogeochemical processes in the ocean. However, the scarcity and sparsity of in situ measurements of Pb concentrations and isotope compositions do not allow for a comprehensive understanding of Pb pollution pathways and biogeochemical cycling on a global scale. Here, we present three machine learning models developed to map seawater Pb concentrations and isotope compositions, leveraging the global GEOTRACES dataset as well as historical data. The models use climatologies of oceanographic and atmospheric variables as features from which to predict Pb concentrations, 206Pb/207Pb, and 208Pb/207Pb. Using SHapley Additive exPlanations (SHAP), we found that seawater temperature, atmospheric dust, atmospheric black carbon, and salinity are the most important features for predicting Pb concentrations. Dissolved oxygen concentration, salinity, temperature, and atmospheric dust are the most important features for predicting 206Pb/207Pb, atmospheric black carbon and dust, seawater temperature, and surface chlorophyll a for 208Pb/207Pb. In line with observations, our model outputs show that (i) the surface Indian Ocean has the highest levels of pollution, (ii) pollution from previous decades is sinking in the North Atlantic and Pacific oceans, and (iii) waters characterised by highly anthropogenic Pb isotope fingerprints are spreading from the Southern Ocean throughout the Southern Hemisphere at intermediate depths. By analysing the uncertainty associated with our maps, we identified the Southern Ocean as the key area to prioritise in future sampling campaigns. Our datasets, models, and their outputs, in the forms of Pb concentrations, 206Pb/207Pb climatologies, and 208Pb/207Pb climatologies, are made freely available to the community by Olivelli et al. (2024a; https://doi.org/10.5281/zenodo.14261154) and Olivelli (2025; https://doi.org/10.5281/zenodo.15355008).