Informed antibiotic prescription offers a practical solution to antibiotic resistance problem. With the increasing affordability of different sequencing technologies, molecular-based resistance prediction would direct proper antibiotic selection and preserve available agents. Amikacin is a broad-spectrum aminoglycoside exhibiting higher clinical efficacy and less resistance rates in Ps. aeruginosa due to its structural nature and its ability to achieve higher serum concentrations at lower therapeutic doses. This study examines the predictive poten- tial of molecular markers underlying amikacin susceptibility phenotypes in order to provide improved diagnostic panels. Using a predictive model, genes and variants underlying ami- kacin resistance have been statistically and functionally explored in a large comprehensive and diverse set of Ps. aeruginosa completely sequenced genomes. Different genes and var- iants have been examined for their predictive potential and functional correlation to amikacin susceptibility phenotypes. Three predictive sets of molecular markers have been identified and can be used in a complementary manner, offering promising molecular diagnostics. armR, nalC, nalD, mexR, mexZ, ampR, rmtD, nalDSer32Asn, fusA1Y552C, fusA1D588G, arnAA170T, and arnDG206C have been identified as the best amikacin resistance predic- tors in Ps. aeruginosa while faoAT385A, nuoGA890T, nuoGA574T, lptAT55A, lptAR62S, pstBR87C, gidBE126G, gidBQ28K, amgSE108Q, and rplYQ41L have been identified as the best amikacin susceptibility predictors. Combining different measures of predictive per- formance together with further functional analysis can help design new and more informa- tive molecular diagnostic panels. This would greatly inform and direct point of care diagnosis and prescription, which would consequently preserve amikacin functionality and usefulness.