In this study, we developed an updated genome-scale model (GEM) of Pseudomonas aeruginosa PA14 and utilized it to showcase the broad capabilities of the GEM. P. aeruginosa is an opportunistic human pathogen that is one of the leading causes of nosocomial infections in hospital settings. We used both automated and manual approaches to reconstruct and curate the model, and then added strain-specific reactions (e.g., phenazine transport and redox metabolism, cofactor metabolism, carnitine metabolism, oxalate production, etc.) after extensive literature review. We validated and improved the model using a set of gene essentiality and substrate utilization data. This effort led to a highly curated, three-compartment and mass-and-charge balanced BiGG model of PA14 that contains 1511 genes and 2036 reactions. Even with considerable increase in model contents (genes, reactions, and metabolites), compared to the previous model (mPA14) of the same strain, this model (iSD1511) has similar prediction accuracy for gene essentiality and higher accuracy for substrate utilization assay. We assessed iSD1511 using another set of gene essentiality and substrate utilization data and computed the prediction accuracies as high as 92.7% and 93.5%, respectively. The model can simulate growth in both aerobic and anaerobic conditions. Finally, we utilized the model to recapitulate the results of multiple case studies including drug potentiation by citric acid cycle intermediates. Overall, we have built a highly curated computational model of the P. aeruginosa to decipher the metabolic mechanisms of drug resistance, and to help in the development of effective intervention strategies.