Balanced Train set to predict Imbalanced Prediction set
It's possible. Consider KNN. When you train a model, it essentially "remembers" the training set. Now, when you use the model to predict an unbalanced data set, the model simply read off from the memory, so there's no problem.

For example, imagine you train a model with an uniformly distributed age group from 10 to 80. Now you have a test set where everybody is 70-80 years old. KNN simply find out the neighbors for your old people (say 60-80 old people in your training data) and then compute a weighted average.

Using a balance training set to predict a imbalanced test set is not super challenging. The other way around is much more challenging.