Abstract: : Improvement on more potent anti-tuberculosis agents is as a result of emergence of multi-drug resistant strains of M. Tuberculosis. Syntheses of novel compounds are usually by trial approach with lots of errors which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. Tuberculosis. This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 1,2,4-triazole analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, the best descriptors that could efficiently predict the activities of the inhibitory agents were selected by employing Genetic Function Approximation (GFA) as a modeling tool. Correlation coefficient (R2) of 0.9142, cross validation coefficient (Q_cv^2) value of 0.8324 and adjusted correlation coefficient (R2 adj) value of 0.8851 were the internal validation test conducted to access the derived model while (R2test) of 0.7495 and Y-randomization Coefficient (cR_p^2) of 0.7334 were the external validation tests to confirmed the robustness of the built model. The proposed QSAR model provides a valuable approach for modification of the lead compound, design and synthesizing of more potent anti-tubercular agents.