Metal-Organic Frameworks (MOFs) were introduced in the early 90s and the number of research projects and reported structures have dramatically increased ever since. Due to their enormous versatility in terms of constituents and possible structures, MOFs are rich in diversity, which makes the task of selecting suitable candidates for a given use from the +90.000 already reported (and over half million predicted) is by no means attainable with experimental work within reasonable time frames. To overcome this issue, nowadays com-mon in materials science, big data and Machine Learning (ML) approaches can be employed to establish properties and applications of different materials, including MOFs. A successful ML approach relies on the existence of appropriate databases. It can provide interesting alternatives for the identification of suitable candidates for various applications or the determination of key structural and functional features, and it has been successfully employed in MOF research over the last years. Another possible alternative is to use ML for systematization and validation of multiple reported synthesis methods for a particular material, as it was recently demonstrated for an archetypal Copper-trimesic acid MOF, HKUST-1. Another example, is the use of linear models to investigate the formation of defects on UiO-66 MOF and their impact on its catalytic and adsorptive performance. This approach provided valuable insights into optimizing defect formation through various synthetic parameters. It becomes clear then that ML and big data are powerful tools to obtain new insight and unveil underlying relations of not only synthesis parameters used for synthesizing MOFs but also for determining their suitability for a given application.