<p>Whole Slide Imaging (WSI) has revolutionized modern pathology by enabling high-resolution digitization of tissue specimens, often exceeding 100,000 × 100,000 pixels, thereby supporting enhanced diagnostic interpretation and telepathology workflows. However, automated analysis of such gigapixel-scale data remains challenging due to computational complexity, staining variability, and morphological heterogeneity across samples. Deep learning-driven segmentation techniques particularly Fully Convolutional Networks (FCN), U-Net, and Mask R-CNN have demonstrated significant advances in tumor detection, cellular boundary delineation, and metastasis identification, with state-of-the-art studies reporting Dice similarity coefficients typically ranging from 0.85 to 0.92 in nuclei segmentation tasks. This review provides a focused and comprehensive overview of segmentation-oriented deep learning methodologies specifically designed for WSI in pathology. We summarize the current workflow including image digitization, patch extraction, data annotation, preprocessing strategies such as stain normalization and ROI enhancement, model selection, post-processing optimization, and clinical integration. By consolidating recent developments and outlining persistent gaps including limitations in labeled datasets, generalizability, hyperparameter sensitivity, and real-time deployment barriers this review offers practical insights to accelerate translation of deep learning segmentation into routine pathology practice. The work is intended as a valuable resource for clinicians, biomedical researchers, and developers engaged in computational pathology and precision diagnostics.</p>