To develop a method for the detection of nitrotyrosine (NTS), transition metals: cobalt (Co), Iron (Fe), and Manganese (Mn) decorated Platinum-doped carbon quantum dot (Pt@CQDs) nanomaterial surfaces were computationally investigated. This study employed density functional theory (DFT) at the d3bj-B3LYP/def2SVP level of theory to gain in-depth knowledge of the adsorption, selectivity, reactivity, and trapping efficacy of nitrotyrosine (NTS) as a biomarker for Alzheimer disease. Results of the adsorption energy of the systems increased as −46.661 kcal/mol, −45.308 kcal/mol, −666.673 kcal/mol, and −12.507 kcal/mol which correspond to NTS_Co_Pt@CQDs, NTS_Fe_Pt@CQDs, NTS_Mn_Pt@CQDs, and NTS_Pt@CQDs. NTS_Mn_Pt@QDs system demonstrated the chemisorption as it has the highest adsorption energy. HOMO-LUMO analysis shows that cobalt decorated surface had the least energy gap of 1.586 eV while interaction with nitrotyrosine a decrease in the energy gap was observed particularly NTS_Pt@CQDs and NTS_Fe_Pt@CQDs exhibiting values of 1.850 eV and 1.665 eV respectively, whereas, Co_Pt@CQDs and Mn_Pt@CQDs systems showed drastic increase in the energy gap to 2.404 eV and 2.719 eV. This collective finding of this study is significant enough to present these systems as potential models for the detection and trapping of nitrotyrosine biomarkers for the diagnosis of Alzheimer's disease.