Gemini surfactant corrosion inhibitor (CI) is one type of CI mainly used in mitigating corrosion in the complex system of oil/gas production industries. Computer modeling methods such as density functional theory (DFT) calculation and molecular dynamic (MD) simulation are required to develop new CI molecules focusing on their application condition as a prediction or screening process before the physical empirical assessment. In this work, the adsorption inhibition efficiencies of two monomer surfactants (2B and H) and their respective Gemini structures with the addition of different spacers (alkyl, benzene, ester, ether, and ketone) are investigated using DFT calculation and MD simulation method in 3% sodium chloride (NaCl), and 1500 ppm acetic acid solutions. In DFT calculation, 2B-benzene molecules are assumed to have the most promising inhibition efficiency based on their high reactivity and electron-donating ability at their electron-rich benzene ring region based on the lowest bandgap energy (0.765 eV) and highest HOMO energy value (−2.879 eV), respectively. DFT calculation results correlate with the adsorption energy calculated from MD simulation, where 2B-benzene is also assumed to work better as a CI molecule with the most adsorption strength towards Fe (110) metal with the highest negative adsorption energy value (−1837.33 kJ/mol at temperature 323 K). Further, diffusion coefficient and molecular aggregation analysis in different CI concentrations through MD simulation reveals that only a small amount of Gemini surfactant CI is needed in the inhibition application compared to its respective monomer. Computer simulation methods successfully predict and screen the Gemini surfactant CI molecules that can work better as a corrosion inhibitor in acetic acid media. The amount of Gemini surfactant CI that needs to be used is also predicted. The future planning or way forward from this study will be the development of the most promising Gemini surfactant CI based on the results from DFT calculation and MD simulations.