Microscopic image analysis using deep learning is a rapidly growing research area in biomedical imaging that focuses on the automated interpretation and quantification of images obtained from microscopes, including cellular, tissue, and subcellular structures. Early approaches relied on handcrafted features and traditional image processing methods, whereas recent research leverages convolutional neural networks (CNNs), U-Net and its variants, and attention-based architectures for tasks such as segmentation, classification, detection, and counting of microscopic structures. Advanced methods also incorporate 3D CNNs for volumetric data, generative models for data augmentation, and graph-based networks for modeling spatial relationships between cellular components. Applications span histopathology, cytology, microbiology, drug discovery, and disease diagnosis, enabling high-throughput, accurate, and reproducible analysis of complex microscopic images. Current research also explores transfer learning, weakly supervised and semi-supervised learning, and multimodal integration with molecular or genomic data to improve performance and generalization in real-world biomedical contexts.