Accurate liver and tumor segmentation from medical imaging plays an important role in effective diagnosis and appropriate treatment planning, especially in the case of liver cancer. This research proposed a novel U-Net architecture enhanced with image colorization techniques for precise liver tumor segmentation in clinical CT images. The proposed image colorization-based U-Net, which integrates both grayscale-based and RGB-based architectures, was tested on the LiTS dataset and real clinical data. This evaluation aimed to measure its effectiveness in liver and tumor segmentation across different imaging conditions. The grayscale-based U-Net achieved high segmentation accuracy, achieving a DICE coefficient of 99.95% for liver segmentation and 90.44% for tumor segmentation. This strong performance suggests its ability to precisely delineate anatomical structures. The model also achieved an RMSE of 0.019, a PSNR of 82.14, and a pixel accuracy of 0.316, reflecting its capability to reduce reconstruction while preserving overall image quality. These findings further support the model’s reliability in challenging imaging scenarios, suggesting its potential as an effective tool for liver tumor segmentation. To further validate its real-world applicability, the model was tested on clinical data, where it effectively segmented liver and tumor regions across diverse imaging conditions. By addressing challenges such as low contrast and variability in tumor characteristics, the use of grayscale-based colorization techniques enhances feature representation, leading to improved segmentation outcomes. The findings demonstrate the potential of the proposed approach to enhance liver and tumor localization, providing a robust framework for clinical applications.