Survey sampling is a widely used technique for collecting data from a subset of a bigger population. Among its methods, stratified random sampling is particularly valuable for yielding precise inferences about distinct subgroups within a population by dividing the population into mutually exclusive strata and sampling from each group. This approach reduces sampling error and enhances the accuracy of population estimates. In this study, we propose a set of improved calibrated log-ratio-type estimators for estimating population variance under a stratified sampling framework. The performance of three proposed estimators is evaluated and compared in terms of the mean squared error. A simulation study is conducted to assess the efficiency of the estimators, complemented by a real-life application to validate the simulation results. The findings demonstrate that the proposed calibrated log-ratio variance estimators outperform existing methods by achieving lower mean squared error.