Prediction of allergic pollen concentration is one of the most important goals of aerobiology. Past studies have used a broad range of modeling techniques; however, the results cannot be directly compared owing to the use of different datasets, validation methods, and evaluation metrics. The main aim of this study was to compare nine statistical modeling techniques using the same dataset. An additional goal was to assess the importance of predictors for the best model. Aerobiological data for Corylus, Alnus, and Betula pollen counts were obtained from nine cities in Poland and covered between five and 16 years of measurements. Meteorological data from the AGRI4CAST project were used as a predictor variables. The results of 243 final models (3 taxa × 9 cities × 9 techniques) were validated using a repeated k-fold cross-validation and compared using relative and absolute performance statistics. Afterward, the variable importance of predictors in the best models was calculated and compared. Simple models performed poorly. On the other hand, regression trees and rule-based models proved to be the most accurate for all of the taxa. Cumulative growing degree days proved to be the single most important predictor variable in the random forest models of Corylus, Alnus, and Betula. Finally, the study suggested potential improvements in aerobiological modeling, such as the application of robust cross-validation techniques and the use of gridded variables.