We developed a high-throughput method combining flow cytometry and machine learning to identify urban pollen. Using a reference database of 97 species across 34 genera, with values of particle size, granularity, and multi-channel fluorescence for each pollen grains, our method enables rapid species- and genus-level pollen identification. It provides an efficient alternative to microscopy, with potential for large-scale urban pollen monitoring and allergy management.