Background: Barrett’s Oesophagus (BO), a metaplastic response to chronic gastro-oesophageal reflux, is a well-established precursor to Oesophageal Adenocarcinoma (OAC) – one of the most prevalent thoracic cancers with rising incidence and poor prognosis in developed countries especially the United Kingdom. This progression from the normal epithelium to BO and ultimately to OAC is driven by complex and multi-layered molecular alterations. While bioinformatics has advanced our understanding of this trajectory, significant gaps remain in integrating multi-omics data, capturing tumour heterogeneity, and building predictive models for disease progression. Therefore, addressing these challenges requires computational approaches that goes beyond traditional single-omics analyses. We present a computational framework designed to address these challenges by: (i) applying AI/ML-driven methods to integrate genomic, transcriptomic, and epigenomic data; (ii) identifying disease relevant molecular signatures; (iii) inferring pathway activity to reveal functionally important mechanisms of progression across omics layers; and (iv) enabling patient risk stratification through integrative modelling. By capturing pathway-level alterations, this framework provides a systems level understanding of disease mechanisms and improves the detection of early molecular changes and prognostic markers.