Colorectal Cancer (CRC) is a main cause of cancer-associated deaths globally, with a significant number of victims due to late diagnosis. There is a correlation between colorectal polyps and the occurrence of CRC, highlighting the need for early intervention and diagnosis. The capability to segment the polyp accurately is critical as it would facilitate timely treatment and improve diagnosis accuracy. However, polyp segmentation presents challenges caused by different sizes, and shapes of the polyps, also in cases when they are concealed beneath the mucosal area. U-Net architecture is used to address these challenges to enhance the accuracy of the polyp identified. Our study utilized the Kvasir-Seg dataset. The U-Net model was trained on this data. The model demonstrated a loss of 0.225 and a validating accuracy of 0.9021. This advanced segmentation technique helps the gastroenterologist identify polyps with high accuracy and ultimately helps the patient with early detection and diagnosis.