The severity and progression of lung disease are highly variable across individuals with cystic fibrosis (CF) and are imperfectly predicted by mutations in the human gene CFTR, lung microbiome variation or other clinical factors. The opportunistic pathogenPseudomonas aeruginosa(Pa) dominates airway infections in most CF adults. Here we hypothesized that within–host genetic variation ofPapopulations would be associated with lung disease severity. To quantifyPagenetic variation within CF sputum samples, we used deep amplicon sequencing (AmpliSeq) of 209Pagenes previously associated with pathogenesis or adaptation to the CF lung. We trained machine learning models usingPasingle nucleotide variants (SNVs), microbiome diversity data and clinical factors to classify lung disease severity at the time of sputum sampling, and to predict lung function decline after 5 years in a cohort of 54 adult CF patients with chronicPainfection. Models usingPaSNVs alone classified lung disease severity with good sensitivity and specificity (area under the receiver operating characteristic curve: AUROC=0.87). Models were less predictive of lung function decline after 5 years (AUROC=0.74) but still significantly better than random. The addition of clinical data, but not sputum microbiome diversity data, yielded only modest improvements in classifying baseline lung function (AUROC=0.92) and predicting lung function decline (AUROC=0.79), suggesting thatPaAmpliSeq data account for most of the predictive value. Our work provides a proof of principle thatPagenetic variation in sputum tracks lung disease severity, moderately predicts lung function decline and could serve as a disease biomarker among CF patients with chronicPainfections.