Antimicrobial resistance (AMR) is an increasing threat according to the World Health Organization. Pseudomonas aeruginosa is a Gram-negative and opportunistic organism that develops multidrug resistance in several ways, which requires a better understanding of the mechanisms and new and effective solutions to overcome AMR. The transcriptome, a complete set of RNA molecules, provides information about gene expression, regulation, and function, leading to the translation of this information for disease diagnosis, treatment, and drug development and discovery. Integration of transcriptome data and a genome-scale metabolic model (GEM) of the organism is one of the useful strategies to identify and understand metabolic activities related to AMR. The discovery of reporter metabolites (RM) and their specific metabolic pathways may be a powerful method to study metabolic responses and cellular mechanisms of AMR under different conditions, leading to potential therapeutic targets or biomarkers. This study integrates transcriptomic data from 414 drug-resistant clinical isolates with a genome-scale metabolic model (GEM) of P. aeruginosa to identify metabolic adaptations under four antibiotic stresses: ceftazidime (CAZ), ciprofloxacin (CIP), meropenem (MEM), and tobramycin (TOB). Differential gene expression (DGE) analysis revealed largely drug-specific responses, with minimal overlap in differentially expressed genes (DEGs) across conditions. Using the Reporter Metabolite (RM) algorithm, metabolites central to the resistance phenotype were identified. Pathway enrichment analysis highlighted antibiotic-specific alterations in metabolic networks, many of which are associated with biofilm formation and virulence. Based on the findings, we propose a novel adjuvant therapy composed of condition-specific metabolites (propionic and acetic acids, L-inositol, glutamine, glutarate, fumarate, and melatonin) to enhance antibiotic efficacy and reduce resistance development. This systems biology approach provides a comprehensive framework for metabolic intervention in AMR pathogens.