The rapid rise of artificial intelligence (AI) in both scientific research and everyday life over the last decade has renewed interest in how AI-driven methods can support fungal research, diagnostics, and clinical microbiology. At the same time, advances in imaging technologies and increasing access to large collections of microscopy and medical imaging data have created new opportunities for automated image analysis. In this review, we examine the recent development of AI-based image analysis methods for microscopy in general, with a particular focus on fungal imaging applications. We discuss how deep learning approaches, especially Convolutional Neural Networks (CNNs) and U-nets, have transformed image classification and object detection tasks by enabling automated recognition of fungal cells, spores, hyphae, and infection-related structures with high accuracy. Finally, we explore emerging developments in the field, particularly the introduction of vision transformers and hybrid transformer-based architectures.