What if the votes for 2 classes are equal in an ensemble learning technique?
Depending on the implementation, this problem never occurs. Most of the implementations build an odd number of trees or models to ensure one class's dominance.
However, some implementations allow an even number of trees and models. In such a case, some algorithms simply just throw an error by mentioning the tie has occurred.
Especially in R, some of the libraries solved this issue by randomly picking one of the classes.
The last and best (I think) solution is choosing the class that is more frequent than another. In other words, the class that is more frequent in the training data is chosen arbitrarily by the algorithm to break the tie.