A fundamental problem in medical image analysis, biomedical image segmentation, is essential to many therapeutic applications. To facilitate quantitative analysis and support disease diagnosis, treatment planning, and disease monitoring, it requires partitioning images into different regions or objects of interest. Artificial intelligence (AI) methods, in particular deep learning algorithms, have become effective tools for biomedical picture segmentation in recent years. This chapter presents a biomedical image segmentation application of AI, emphasizing its potential to enhance precision, effectiveness, and therapeutic results. It examines several AI techniques, such as generative models and convolutional neural networks, and how they might be used to tackle the difficulties associated with image segmentation. It emphasizes how using AI algorithms can produce precise and reliable segmentation results. We go over the difficulties with biomedical image segmentation as well as the improvements made feasible by AI methods. We also implement a medical image segmentation example with TransResUNet. The impact of AI on biomedical image segmentation and its promise to alter medical imaging and customized healthcare are highlighted in the chapter's conclusion.