Gastrointestinal cancers, including esophageal, gastric, colorectal, liver, gallbladder, cholangiocarcinoma, and pancreatic cancers, pose a significant global health challenge due to their high mortality rates and poor prognosis, particularly when diagnosed at advanced stages. These malignancies, characterized by diverse clinical presentations and etiologies, require innovative approaches for improved management. Bayesian networks (BN) have emerged as a powerful tool in this field, offering the ability to manage uncertainty, integrate heterogeneous data sources, and support clinical decision-making. This review explores the application of BN in addressing critical challenges in gastrointestinal cancers, including the identification of risk factors, early detection, treatment optimization, and prognosis prediction. By integrating genetic predispositions, lifestyle factors, and clinical data, BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies. Despite their promise, the widespread adoption of BN is hindered by challenges such as data quality limitations, computational complexities, and the need for greater clinical acceptance. The review concludes with future research directions, emphasizing the development of advanced BN algorithms, the integration of multi-omics data, and strategies to ensure clinical applicability, aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.