The understanding and management of infectious diseases depend heavily on the implementation of genomic surveillance techniques for airborne pathogens. These cutting-edge methods utilize advanced sequencing technologies and sophisticated algorithms to meticulously track genetic variations in airborne pathogens, such as bacteria and viruses. By systematically analyzing genomic data, scientists can monitor the progression and alterations in pathogen genomes over time, providing invaluable insights into the emergence of new strains, patterns of transmission, and evolutionary pathways. Genomic surveillance has become a pivotal approach in understanding the evolution and spread of airborne pathogens, enabling the development of targeted intervention strategies. Molecular docking studies play a crucial role in drug discovery by predicting the binding affinity of ligands to target proteins. This study evaluates docking scores obtained from CB-Dock and SwissDock for various protein-ligand interactions related to Mycobacterium tuberculosis (M. tuberculosis), Bacillus anthracis (B. anthracis), Bordetella pertussis, and Haemophilus influenzae. The results highlight variations in docking scores across different tools, reflecting differences in scoring functions and algorithms. A comparative analysis provides insights into the effectiveness of computational docking in identifying potential inhibitors for infectious diseases. Additionally, this study emphasizes the importance of cross-validation in computational docking and the need for further experimental validation to ensure the accuracy of predictions. Understanding these variations can aid in refining molecular docking methodologies and improving the identification of promising drug candidates.