Leishmaniasis is a neglected disease caused by protozoa belonging to the Leishmania genus. The search for effective anti-Leishmanial compounds is challenging due to high costs, time requirements, and long-term treatment. However, today, with the help of computational approaches, we can undertake projects with lower costs and shorter timelines. The objective of this study was to discover novel and potent anti-Leishmanial compounds and establish the real relationship between the structure of these compounds and their biological activity. To achieve this goal, a total of 26 chemical compounds underwent QSAR analysis. After data preprocessing, a suitable set of molecular descriptors was calculated, and significant descriptors were selected. The Stepwise-MLR model identified nine important descriptors that influenced the activity of the chemical compounds: ” EEig01x,” ” IVDE,” ” G2m,” ” Mor16m,” ” JGI5,” ” E2e,” ” H0e,” ” G1p,” and ” RDF030m.” The developed model was evaluated based on the correlation between observed and predicted values (R=0.97), the correlation coefficient (R ²=0.94), and the root mean square error (RMSE=0.211). Additionally, molecular docking using AutoDock Vina was employed to explore the binding modes of 26 compounds with Leishmania major N-myristoyltransferase. The binding energy fit score of the best inhibitor, inhibitor 8, was –9.6 kcal/mol. For further investigation, the docked structure obtained from the AutoDock results underwent a molecular dynamics (MD) simulation for 100 ns. Analysis of the trajectory file confirmed the efficacy of the final compound against Leishmania. This study marks a significant step in the development of drug candidates against Leishmaniasis.