Background: Antimicrobial resistance (AMR) remains a global health crisis, driving the need for innovative approaches to identify druggable targets and develop effective therapies. Conventional strategies are often insufficient to address the rapid evolution of resistance, particularly in high-priority pathogens. This study combines artificial intelligence (AI)-driven gene prioritization with nanotechnology to combat multidrug-resistant bacterial infections. Methods: We employed a dual-axis strategy that integrates genetic algorithm based selection to evaluate gene centrality, conservation, and interaction networks, which has proven effective in predicting the druggability of AMR genes such as blaNDM1 and mcr1. To facilitate pH responsive ciprofloxacin delivery, hydroxyapatite-alginate-chitosan nanocapsules were synthesized. Molecular docking studies validated ligand-target compatibility, and the physical characteristics of the nanocapsules were analyzed using dynamic light scattering (DLS) and scanning electron microscopy (SEM), revealing a particle size range of 100 to 200 nanometers. In vitro drug release kinetics were evaluated over a 48-hour period. Results: The druggability score for blaNDM-1 was 0.92, while mcr1 scored 0.89, positioning both genes as high-priority targets due to their roles in lactam and colistin resistance mechanisms in E. coli and Pseudomonas aeruginosa. The nanocapsules had a mean size of 150 nanometers (Plus or Minus of 25 nm) and showed sustained ciprofloxacin release, with 75% of the drug released at the 24 hour mark. Docking studies confirmed stable binding of ciprofloxacin to the active sites of the target proteins, with hydrogen bond distances less than 2.0 A, supporting its potential as an inhibitor. Conclusions: Our findings demonstrate the utility of computational and experimental strategies in combating AMR. The AI driven nanotherapy pipeline successfully identified high-value therapeutic targets and validated the efficacy of nanoparticle-based drug delivery systems, achieving a 6 log reduction in bacterial load in vitro. Future work will focus on in vivo validation and the clinical translation of these findings.