This paper addresses the issue of estimating population variance by incorporating auxiliary information within the framework of a simple random sampling without a replacement scheme. We have suggested classes of estimators for population variance (S2y) of the study variable (Y) utilizing information available on the population variance (S2x) of the auxiliary variable (X). Expressions for the bias and mean squared error (MSE) for the suggested estimators up to the first order of approximation have been obtained. Furthermore, we have established optimal conditions under which these new estimators outperform certain existing ones. An empirical study using three real population datasets has been conducted to substantiate the theoretical results.