Machine learning (ML) and artificial intelligence (AI) have revolutionized microscopy image analysis, enabling more accurate, efficient, and automated interpretations of complex biological data. This chapter shows practical examples in Python and discusses the application of ML, particularly deep learning, in veterinary pathology, with a focus on histopathological image analysis. Convolutional neural networks (CNNs), a subset of deep learning, have shown great promise in classifying tissue samples, detecting cellular structures, and identifying pathological changes without requiring manual feature extraction. Techniques such as U-Net facilitate precise image segmentation, crucial for quantifying cellular structures in dense tissues. Additionally, generative adversarial networks (GANs) are used to enhance image resolution, improving diagnostic accuracy. A key challenge remains in the availability of large, high-quality annotated datasets necessary for training ML models, though transfer learning offers a solution by enabling models pretrained on large datasets to be adapted for histopathological data. Despite their accuracy, deep learning models often operate as “black boxes,” spurring the development of explainable AI techniques like Grad-CAM, which provide visual interpretations of model decisions. These advancements not only enhance diagnostic precision but also increase the transparency and trustworthiness of AI systems in veterinary diagnostics, marking a significant shift in the field.