This chapter presents current and potential future clinical applications of artificial intelligence (AI) in the management of autoimmune inflammatory bullous dermatoses. Recent studies indicated that AI-powered image recognition convolutional neural networks (CNNs) have shown promising potential to improve or advance various stages of diagnosis in this category of skin entities even among dermatoses with similar bullous-like appearance, differentiating pemphigus vulgaris, and bullous pemphigoid. Also, the automated AI-assisted laboratory techniques as in the case of direct and indirect immunofluorescence and the TzanckNet are based on CNNs that correlate laboratory images with certain diagnoses. There is currently no comprehensive study focusing on AI-powered assessment grading of bullous disorders, bullous dermoscopy analysis, or AI-assisted treatment planning. Additionally, databases containing diverse bullous dermatosis images remain limited, restricting AI training systems and reducing their ability to differentiate between subtypes effectively. Skin tone variability and systemic involvement commonly encountered in those diseases should be considered when training AI models, and the reflected lighting to the smooth, fluid-filled surface of the bulla when photographing is a technical issue that may lead to AI misinterpretations. More studies are warranted that would lead to advanced AI tools that would be integrated into clinical practice and guide clinicians to a better approach to bullous dermatoses.