Significant populations in tropical and sub-tropical locations all over the world are severely impacted by a group of neglected tropical diseases called leishmaniases. This disease is caused by roughly 20 species of the protozoan parasite from the Leishmania genus. Disease prevention strategies that include early detection, vector control, treatment of affected individuals, and vaccination are all essential. The diagnosis is critical for selecting methods of therapy, preventing transmission of the disease, and minimizing symptoms so that the affected individual can have a better quality of life. Nevertheless, the diagnostic methods do eventually have limitations, and there is no established gold standard. Some disadvantages include the existence of cross-reactions with other species, and limited sensitivity and specificity, which are mostly determined by the type of antigen used to perform the tests. A viable alternative for a more precise diagnosis is the application of recombinant antigens, which have been generated using bioinformatics approaches and have shown increased diagnostic accuracy. This approach proves valuable as it spans from epitope selection to predicting the interactions within the antibody–antigen complex through docking analysis. As a result, identifying potential new antigens using bioinformatics resources becomes an effective technique since it may result in an earlier and more accurate diagnosis. Consequently, the primary aim of this review is to conduct a comprehensive overview of the most significant in silico tools developed over time, with a focus on evaluating their efficacy and exploring their potential applications in optimizing the selection of highly specific molecules for a more effective diagnosis of leishmaniasis.