Amine-templated metal oxides are a class of hybrid organic-inorganic compounds with great structural diversity; by varying the compositions, 0D, 1D, 2D, and 3D inorganic dimensionalities can be achieved. In this work, we created a dataset of 3725 amine-templated metal oxides (including some metalloid oxides), their composition, amine identity, and dimensionality, extracted from the Cambridge Structure Database (CSD), which spans 71 elements, 25 main group building units, and 349 amines. We characterize the diversity of this dataset over reactants and in time. Artificial neural network models trained on this dataset can predict the most and least probable outcome dimensionalities with 71% and 95% accuracies, respectively, using only information about reactant identities, without stoichiometric information. Surprisingly, the amine identity plays only a minor role in most cases, as omitting this information only reduces the accuracy by <2%. The generality of this model is demonstrated on a time held-out test set of 36 amine-templated lanthanide oxalates, vanadium tellurites, vanadium selenites, vanadates, molybdates, and molybdenum sulfates, whose syntheses and structural characterizations are reported here for the first time, and which contain two new element combinations and four amines that are not present in the CSD.