The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical ap- proaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We sur- vey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a crit- ical look at the use of ML in CH and consider why CH has only limited adoption of ML.