<p>Atmospheric ultrafine particles (UFPs) dominate the number concentration of ambient particulate matters and have potentially high health effects. Their ultrafine size (&lt; 100 nm) results in extremely low mass and weak light-scattering signals, thereby limiting the applicability of measurement techniques used for fine particles (PM_2.5). This review synthesizes the existing techniques developed to characterize the complex properties of UFPs, including concentration, size distribution, chemical composition, and morphology. Despite existing UFP field observations, challenges remain in developing a comprehensive understanding of global UFP properties. Inconsistencies in instrumentation and critical parameters across different campaigns hinder data comparability, underscoring the need for standardized measurement protocols. Furthermore, current UFP monitoring networks primarily focus on concentration and size distribution, with other key properties, like chemical composition, remaining largely outside routine monitoring and limited to dedicated research campaigns or supersites. Machine learning approaches offer a promising avenue to integrate independent studies, enabling a more comprehensive understanding of global UFP properties. Such comprehensive characterization is essential for accurately assessing UFP exposure risks and climate impacts. These insights are urgently needed to strengthen existing particle-number-concentration-based regulatory frameworks and develop more effective air quality guidelines and emission control strategies.</p>