Abstract: This study presents a comparative analysis of tight band imaging (NBI) and hyperspectral imaging (HSI) for early and accurate detection of small intestinal ulcers. Small intestinal ulcers, often caused by diseases such as Crohn's disease, NAID use, and bacterial infections, can lead to serious complications if not diagnosed early. Traditional imaging techniques often recognize subtle changes in the mucosa at the early stages. This study evaluates the strength of NBI in improving vascular visualization and the ability to recognize biochemical and structural variation at the tissue level. Through clinical imaging, data processing, and machine learning analyses, this study evaluates both modalities in terms of sensitivity, specificity, and diagnostic accuracy. The results show that HSI offers excellent diagnostic performance, but NBI is advantageous for practical visualization. The improved artificial intelligence through the integration of both methods indicates the important potential for gastrointestinal diagnosis conversion. The integration of both imaging methods through AI enhances early detection,improves targeted biopsies, and ultimately leads to better patient outcomes. Keywords: Small Intestine Ulcers, Narrow Band Imaging (NBI), Hyperspectral Imaging (HSI), Gastrointestinal Diagnostics, Machine Learning, Early Detection, Vascular Mapping, Spectral Analysis