Non-prediction Applications of Machine learning
The most obvious applications are indeed the supervised learning approaches (surrogate models, prediction). But there is much more than that! Other usual applications include:


Clustering: This can be seen as a different kind of prediction, but not in the classic supervised learning fashion. For instance, I have been using a clustering algorithm on a 3D geometry (CAD file) to make almost adjacent elements become actually aligned. 
Anomaly detection
Study of extreme values (how to learn the behaviour of extreme values without actually observing such values)
Feature importance (determine which sets of features impacts the result the most) or, more generally, data mining
Inference (use a dataset to improve the prior model prediction)


Of course, most of these applications are more or less close to prediction, but not trivial prediction.