In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, causing pandemics and outbreaks with increased virulence, resistance, spreading ability and disease severity. However, our understanding of the genomic determinants influencing transmission and disease severity patterns, as well as their interplay, remains incomplete. Here, we developed a computational framework based on machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae signatures genomic traits linked to lineage transmission dynamics and disease severity. We analysed isolates collected from in-patients across six regions in Bangladesh from 2015 to 2021, and uncovered a core set of accessory genes, coding, and intergenic SNPs uniquely present in the most recent dominant lineage and underlying lineage transmission, with virulence, motility, colonization, biofilm formation, acid tolerance and bacteriophage resistance functions. Furthermore, we uncovered the existence of a strong correlation between a core set of V. cholerae genomic determinants and disease severity patterns (diarrhoeal duration, number of stools, abdominal pain, vomit, and dehydration). A subset of these determinants overlapped with those driving lineage transmission dynamics. Through GSMM and 3D structure analysis, we inferred the mechanistic bases underlying their selection and unveiled a complex interplay between transcription regulation, protein interactions and stability, and metabolic networks leading to severe symptoms. These connections influence lifestyle adaptation, intestinal colonization, oxidative stress and acid tolerance through modulation of ribosome, fatty acid and peptide biosynthesis, bacterial efflux systems, virulence, and resistant genes. Our computational framework allows to uncover signature traits which can provide insights for advancing therapeutics and developing targeted interventions to mitigate cholera spread.