Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tickborne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used feld data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly infuenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a frst step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.