Tick-borne diseases (TBDs) are increasing globally due to factors such as climate and land use change. Accurate forecasting of questing tick densities can allow the public to make informed decisions to reduce the risk of TBD exposure. However, ticks have a multi-stage life cycle that can potentially be affected by factors such as the population dynamics of their small mammal hosts, weather, and land cover, making accurate forecasts difficult and computationally expensive. We addressed these problems using a hierarchical state-space model, which leverages data from tick surveys, small mammal surveys, and meteorological data across the eastern United States to forecast questing tick densities for each life stage up to a year in advance. We applied the model to two tick species, Ixodes scapularis and Amblyomma americanum, which are vectors of TBDs in North America. We expected that population sizes of the small mammal host Peromyscus leucopus would impact the transition success rate between life stages, but mouse variables were generally poor predictors of questing tick densities based on the 95% CI. We found that several meteorological variables, especially precipitation and relative humidity, affected tick survival; however, their importance varied based on tick species, life stage, and geographic location. In particular, the survival of nymphs and larvae tended to have strong associations with relative humidity and precipitation, whereas these variables did not have clear associations with adult survival. Our modeling framework can assist in making public health decisions by predicting changes in tick questing activity in response to a changing climate.