<p>Sample survey involves selecting a representative subset of individuals from a larger population to gather information and make inference about the entire target population. The use of auxiliary information while estimating the population parameters are widely adapted due to its capability of obtaining efficient estimators. The calibration approach is one of the most employed method for integrating auxiliary information into the estimation process of survey sampling. Rank-based calibration leverages a single auxiliary variable in two different ways. Several researchers have successfully applied rank-based calibration to improve the estimators for uni-stage sampling designs which were merely used in real life surveys as in real life surveys commonly employed designs are multistage in nature. In this context, a new calibration estimator was developed for two-stage sampling design by utilizing an auxiliary variable in two different ways i.e. the auxiliary information itself and its rank. It was also assumed that auxiliary information is available for all the elements in the population. The approximate mean square error and estimator of the approximate mean square error were also derived using Taylor Series linearization technique. The proposed estimator was evaluated against existing estimators using both simulated and real survey data, employing newly developed R code for the analysis. The results demonstrated that the proposed estimator outperformed the existing ones based on the relative and theoretical efficiency measures.</p>