Atmospheric pollution is a major concern due to its well-documented and detrimental impacts on human health, with millions of excess deaths attributed to it annually. Particulate matter (PM), comprising airborne pollutants in the form of solid and liquid particles suspended in the air, has been particularly concerning. Historically, research has focused on PM with an aerodynamic diameter less than 10 <em>&mu;</em>m (PM₁₀) and 2.5 <em>&mu;</em>m (PM_2.5), referred to as coarse and fine particulate matter, respectively. The long term exposure to both classes of PM have been shown to impact human health, being linked to a range of respiratory and cardiovascular complications. Recently, attention has been drawn to the lower end of the size distribution, specifically <em>ultrafine particles</em> (UFPs), with an aerodynamic diameter less than 100 nm (PM_0.1). UFPs can deeply penetrate the respiratory system, reach the bloodstream, and have been increasingly associated with chronic health conditions, including cardiovascular disease. Accurate mapping of UFP concentrations at high spatial resolution is crucial considering strong gradients near the sources. However, due to the relatively recent focus on this class of PM, there is a scarcity of long-term measurements, particularly on the global scale. In this study, we employed a machine learning methodology to produce the first global maps of UFP concentrations at high spatial resolution (1 km) by leveraging limited ground station measurements worldwide. We trained an XGBoost model to predict annual UFP concentrations for a decade (2010&ndash;2019) and utilized the conformal prediction framework to provide reliable prediction intervals. This approach makes local-to-global UFP data available to support assessments of the health implications associated with long-term exposure.