In this thesis we look at three different algorithms within the field of phylogenetics and create a proof of concept for using machine learning to improve the algorithms. The problems are the maximum agreement forest problem, the hybridization number problem and finally the tail move problem. A study of the problems show that they can all benefit from machine learning. For each of the problems we find a machine leanring implementation using basic decision trees. The results show that the MAF problem and the tail move problem can both benefit from machine learning and for the hybridization number problem we give an implementation that is faster than the studied algorithm.