Aerosol particles larger than roughly 50–100 nm in diameter are climatically important because they can act as cloud condensation nuclei (CCN), making their global number concentrations essential for understanding aerosol–cloud interactions. However, observationally constrained, long-term global datasets of particle number concentrations in this size range remain scarce. In this investigation, we present a global dataset of ground-level particle number concentrations for the period 2003–2024, produced by combining in situ observations with a machine-learning approach. The dataset includes two variables: the number concentrations for particles larger than 100 nm (<em>N</em><sub>100</sub>) and larger than 50 nm (<em>N</em><sub>50</sub>), provided at 0.75° × 0.75° spatial resolution and daily temporal resolution. To generate this dataset, we trained an eXtreme Gradient Boosting (XGB) model using measurements from 62 in situ stations as targets and reanalysis variables as predictors, enabling a data-driven representation of particle number concentrations at the global scale. We evaluated the dataset against independent observations from 12 additional stations. At 2/3 of these stations, the dataset shows good performance, capturing the median concentrations within a factor of 1.5 from the observations. Furthermore, we describe the main characteristics of the dataset in terms of global spatial patterns, temporal variability, and seasonal cycles, and demonstrate its ability to capture long-term trends in particle number concentrations, including both increasing and decreasing tendencies reported in the literature. This work provides the first observation-constrained, machine-learning-based global dataset of <em>N</em><sub>50</sub> and <em>N</em><sub>100</sub> at daily resolution over two decades, bridging the gap between sparse measurements and computationally expensive process-based models. The dataset, publicly available at <a href="https://doi.org/10.5281/zenodo.20202080" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20202080</a>, offers a valuable resource for evaluating model simulations, improving CCN-related parameterizations, and supporting weather and climate studies without the need for explicit knowledge of the aerosol particle microphysics.