Hydrophobins are a family of small fungal proteins that self-assemble at hydrophobic-hydrophilic interfaces. Hydrophobins not only play crucial roles in filamentous fungal growth and development but also have attracted substantial attention due to their unique material properties. Structural characterization of class I and class II hydrophobins to date has been limited to a handful of proteins. While machine-learning-based structure prediction methods have the potential to exponentially expand our ability to define global structure-function relationships of biomolecules, they have not yet been extensively applied to hydrophobins. Here, we apply a suite of bioinformatics tools including Rosetta, AlphaFold, FoldMason, and Foldseek toward analysis, modeling, classification, and global comparison of class I and class II hydrophobins. We first probe the structural and energetic features of experimental class I and class II structures available in the Protein Data Bank. Using previously solved X-ray and NMR structures, we benchmark the ability of AlphaFold to predict class I and class II hydrophobin folds. We explore the physicochemical properties of more than 7,000 class I and class II hydrophobins in the UniProt database. Then, using AlphaFold models, we classify the structural universe of all known class I and class II hydrophobins into six distinct clades. We also uncover putative non-canonical features of hydrophobins, including extended N-terminal tails, five disulfide bonds, polyhydrophobins, and non-hydrophobin proteins containing hydrophobin-like folds. Finally, we examine the ability of AlphaFold and Chai-1 to model hydrophobin membrane binding, conformational changes, and self-assembly of class I rodlets and class II meshes. Together, our results highlight that AlphaFold not only accurately models and enables the global comparison of features within the hydrophobin protein family but also uncovers new properties that can be further evaluated with experimentation.