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This investigation aimed to explore deep learning (DL) models’ potential for diagnosing <i>Pseudomonas</i> keratitis using external eye images. In the retrospective research, the images of bacterial keratitis (BK, <i>n</i> = 929), classified as <i>Pseudomonas</i> (<i>n</i> = 618) and non-<i>Pseudomonas</i> (<i>n</i> = 311) keratitis, were collected. Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. Five-fold cross-validation was used to determine the ability of single and ensemble models to diagnose <i>Pseudomonas</i> keratitis. The EfficientNet B2 model had the highest accuracy (71.2%) of the eight single-DL models, while the best ensemble 4-DL model showed the highest accuracy (72.1%) among the ensemble models. However, no statistical difference was shown in the area under the receiver operating characteristic curve and diagnostic accuracy among these single-DL models and among the four best ensemble models. As a proof of concept, the DL approach, via external eye photos, could assist in identifying <i>Pseudomonas</i> keratitis from BK patients. All the best ensemble models can enhance the performance of constituent DL models in diagnosing <i>Pseudomonas</i> keratitis, but the enhancement effect appears to be limited.