One of the biggest challenges to quality of life for an individual with Cystic Fibrosis (CF) is the high rate of incidence of infection with the opportunistic pathogen Pseudomonas aeruginosa. Infections are typically lifelong, causing significant morbidity and mortality, and lead to high rates of divergent within-host evolution, often resulting in the presence of multiple infection phenotypes that are multidrug resistant. This renders traditional therapies and interventions ineffective, with no efficacious vaccine available to date. We profiled within-host adaptation by investigating a pair of isogenic clonal epidemic isolates (AES-1R and AES-1M), isolated from the same patient 11 years apart. Using an integrated multi-omic strategy, we defined cellular differences by parallel proteomics, metabolomics and lipidomics when grown in an artificial sputum-like medium that reflects the physiology of the CF lung. This near-complete cellular characterisation was followed by time-resolved proteomics within the same media to identify proteins crucial to initiation of infection as possible vaccine candidates. Candidate selection was further refined with the aid of the comparative integrated multi-omics data, known recognition by human sera, in vivo expression and comprehensive bioinformatic characterisation. Three proteins were identified, synthesised and subsequently screened for protection against pulmonary P. aeruginosa infection in mice, as well as defining immune correlates. We demonstrated high levels of protective efficacy for two out of three protein vaccines assessed. Presented is a workflow for identification and optimisation of vaccine candidates that is scalable, effective and widely applicable to other culturable bacterial species.