A central distinction between supervised and unsupervised learning methods for XRD analysis is that supervised methods require the prior analysis of the structural data of interest, whereas unsupervised models may be applied directly to XRD data. When obtaining crystal structures from databases like ICSD, pattern fitting or single crystal analysis has already been done and is ready for training of supervised learning models. If the goal is to apply ML to analyze experimental data, then a small representative subset of the original data must be selected and analyzed to train an effective surrogate model. Alternatively, a representative dataset must be constructed from simulated data. Supervised models are the first ML algorithms discussed in this Review. However, when structural analysis or pattern fitting is difficult-such is often the case with novel beamline experiments, in-situ studies, and high-throughput datasets, or when methods do not exist to simulate a representative dataset-unsupervised methods are chosen to visualize the relationships obscured by the dimensionality of the data. Unsupervised methods are the second class of ML models discussed. Often, supervised and unsupervised methods can be complimentary, especially to reduce the dimensionality of data before surrogate model training or to quantity model certainty.