Molecular in-silico docking of the influenza virus protein using docking software with various drugs aims to study drug-target interactions. This approach identifies drug molecules through virtual screening via molecular docking. Recognized as a fast and cost-effective technique, molecular docking analyses the conformation and orientation of atoms or molecules within the binding site of a macromolecular target. Biochemists have developed various models over the years to identify key elements of the molecular process. Docking methods are categorized based on the degrees of flexibility of the molecules being investigated. Interactions between small molecules, such as ligands, and proteins (which may be enzymes) can predict the activation or inhibition of the target enzyme. This review follows the main theories of lock and key, induced-fit, and conformational ensemble. There are three primary types of scoring functions used in molecular docking: force-field-based scoring functions, empirical scoring functions, and knowledge-based scoring functions. By expanding pharmacodynamic information such as strength, viability, selectivity, pharmacokinetic properties, absorption, distribution, metabolism, excretion, and toxicity (ADMET) docking software can enhance drug design. This review will explore how molecular docking optimizes drug design by identifying the best target site of the protein for the ligand, ultimately enhancing ligand-receptor binding before developing the drug.