The aim of the present investigation is to identify effective anti-HIV drugs through the in-silico virtual screening of the coumarin pharmacophore with or without substituents. Virtual screening started with target identification through computation docking and interactions, binding affinity through molecular dynamics, and the ADMET profile through the use of various enzymes. The target study suggests that the target is involved in various stages of HIV replication and in determining the ways in which non-nucleoside reverse transcriptase inhibitors (RTIs) influence it. The interaction pattern and simulation study conclude the specific affinity of coumarin pharmacophore to the HIV's reverse transcriptase enzyme, especially 3HVT. Moreover, the amide linkage worked as a synergistic bridge to provide more interaction to the pharmacophore. The initial results led to the determination of 83 virtual amide-like molecules, which were screened through docking and MD studies (100 ns) on the best-suited enzyme HIV's reverse transcriptase enzyme, such as PDB ID "3HVT". The virtual screening study revealed the high affinity of compounds 7d and 7e with the lowest IC₅₀ values of 0.729 and 0.658 µM; moreover, their metabolism pattern study, toxicity, and QED values in a range of 0.31-0.40 support a good drug candidate. The two compounds were also synthesized and characterized for future in vitro and in vivo studies. The in silico-based descriptor of compounds 7d and 7e indicates the potential future and provides the best two molecules and their synthetic route for the development of a more effective drug to combat HIV/AIDS epidemics.