This research paper explores the application of deep learning methodologies for image segmentation in the context of medical imaging. Recognizing the critical role of precise segmentation in medical diagnostics and treatment planning, the study delves into the advancements of deep neural networks, such as U-Net, FCN, and DeepLab, in accurately delineating anatomical structures or pathological regions. Leveraging a comprehensive review of existing literature, the paper identifies challenges inherent in traditional segmentation methods and underscores the potential of deep learning to address these limitations. The proposed methodology encompasses the detailed description of the chosen deep learning architecture, the datasets employed for training and evaluation, and any preprocessing or augmentation techniques applied. Through rigorous experimentation, the results showcase the efficacy of the deep learning approach, both quantitatively and qualitatively, with comparisons to state-of-the-art methods. The discussion section critically analyzes findings, addressing challenges encountered and proposing avenues for future enhancements. In conclusion, this research contributes to the evolving landscape of medical image segmentation by providing valuable insights into the capabilities and potential improvements of deep learning techniques.