Objectives: This study aims to develop an advanced and efficient deep learning-based approach for the detection and segmentation of cell nuclei in microscopic images. By exploiting the U-Net architecture, this research helps to overcome the limitations of traditionally followed computational methods, enhancing the precision and scalability of biomedical image analysis. Methods: This research utilizes a deep learning model based on the U-Net architecture and is trained and evaluated for cell nuclei segmentation. The model was optimized by fine-tuning parameters, i.e., applying data augmentation techniques and employing performance metrics such as Intersection over Union (IoU) for evaluation. Comparisons were made with traditional segmentation techniques to assess improvements in accuracy, efficiency, and robustness. Results: This U-Net model demonstrated superior performance in segmenting cell nuclei compared to conventional methods. The results showed increased segmentation accuracy, lowering manual efforts, and enhanced reproducibility across different imaging datasets. The model's high IoU values confirmed its effectiveness in accurately identifying cell nuclei boundaries, making it a reliable tool for automated biomedical image analysis. Conclusions: The study highlights the effectiveness of the U-Net architecture in automated cell nuclei detection and segmentation, addressing challenges associated with manual analysis. Its scalability and adaptability extend its applicability beyond cell nuclei segmentation to other biomedical imaging tasks, offering significant potential for disease diagnosis, therapeutic development, and clinical decision-making. The findings reinforce the transformative impact of deep learning in biomedical research and healthcare applications.