<![CDATA[The global incidence of Gastrointestinal (GI) disorders has risen dramatically over recent decades, driven chiefly by changes in dietary patterns and lifestyle behaviours; epidemiological evidence attributes nearly two million deaths annually to these conditions, underscoring their substantial burden on healthcare systems. Despite endoscopy’s status as the diagnostic standard for detecting mucosal lesions—such as adenomatous polyps and oesophagitis— its performance is hindered by observer variability, limited reproducibility, and lengthy procedural times. To address these limitations, computer-aided diagnostic (CAD) frameworks have been integrated into clinical workflows, offering enhanced accuracy, throughput, and operational efficiency. AI-based pipelines leveraging advanced Machine Learning (ML) and Deep Learning (DL) architectures have proven highly effective in the early detection of GI malignancies and in quantitatively assessing tumour invasion depth. These technologies not only accelerate critical clinical decisions but also support the development of individualized, precision oncology regimens. This survey provides an in-depth assessment of current ML and ML methodologies applied to GI and oesophageal cancer diagnostics, evaluates established prognostic biomarkers, compares algorithmic performance metrics, and identifies key research directions to overcome existing methodological and translational challenges. Although AI-driven diagnostic systems hold the potential to transform GI oncology by standardizing workflows and improving detection rates, their routine clinical adoption requires rigorous validation in multicentre trials and the establishment of comprehensive implementation guidelines.]]>
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