Antibiotic resistance has become a growing medical problem in the past years due to the resistance shown by numerous microorganisms that are potential disease-causing agents. This developed resistance makes it hard to treat and prevent infections. It is vital to understand the genetic mechanisms behind resistance to address this problem. Pseudomonas aeruginosa is a motile, non-fermenting, Gram-negative bacterium. It is an opportunistic pathogen implicated in respiratory and urinary tract infections, particularly in immunocompromised patients. It is a multi-drug resistant pathogen that can quickly acquire new resistance traits. This study aims to identify genes and factors responsible for antibiotic resistance in P. aeruginosa. A previously generated insertion sequencing (INSeq) dataset consisting of 50 samples from a high-throughput transposon mutant screen was analyzed using the in-house developed CAFE (Coefficient-based Analysis of Fitness by read Enrichment) protocol. The INSeq dataset consisted of previously prepared samples of five biological replicates from P. aeruginosa overnight cultures that were grown under exposure to sublethal levels of the antibiotics tetracycline and ciprofloxacin, as well as control samples without antibiotic exposure. Both the planktonic and biofilm phases were sampled from each treatment. Following the CAFE analysis, a network analysis using two different networks, KEGG (Kyoto encyclopedia of genes and genomes) and GSMM (Genome-scale metabolic model), was performed. KEGG is a pathway-based network and GSMM is based on genes connected by sharing metabolites. In these two networks, the genes with significantly positive fitness coefficients were identified, and their connected genes were extracted. A subset of these genes was tested in an antibiotic exposure assay to verify their involvement in antibiotic resistance. A priority gene list containing 67 genes of planktonic and biofilm phases in ciprofloxacin and tetracycline were identified using the CAFE protocol where network analysis on these genes by KEGG and GSMM resulted in twelve resistance candidate genes, and 117 metabolically connected genes with similar response patterns were investigated by network analysis. Out of these genes, seven candidate and eleven metabolically connected deletion mutants were verified in the antibiotic exposure essay comparing single mutants to the wild type (MPAO1). The experimental work resulted in confirmation of the resistance function for six candidate and ten connected genes. The twelve resistance gene candidates and their metabolically connected genes were also statistically analyzed by Pearson’s correlation test to check if they function as a single gene or together as a pathway that can help in producing future antibiotic targets. In conclusion, the findings suggest that out of a total of 18 genes (both candidate and connected), 16 genes are involved in ciprofloxacin resistance. Furthermore, out of twelve resistance gene candidates, nine showed a pathway effect, while three had a gene-specific effect. The study highlights that P. aeruginosa carries multiple resistance mechanisms, many of which have not been previously linked to antibiotic resistance. The pathways identified in this work could be potential drug targets for future antibiotics treatment.