<p>Ranked Set Sampling (RSS) is known for its efficiency in parameter estimation, especially when ranking is more feasible than actual measurement. This study introduces a novel memory type estimator for RSS based on Hybrid Exponentially Weighted Moving Averages (HEWMA), using two auxiliary variables. The estimator aims to enhance efficiency by integrating current and past information along with secondary auxiliary data. Analytical expressions for the bias and mean square error (MSE) are derived, and the corresponding sample coefficients are obtained to simplify HEWMA weight calculations. A simulation study is conducted to evaluate the estimator under various conditions, including different sample sizes, distributional shapes (normal and skewed Weibull), and varying correlation levels among study and auxiliary variables. Results indicate that the proposed estimator consistently yields the highest relative efficiency (RE) compared to conventional and memory estimators using one or multiple auxiliary variables. Additionally, the estimator is applied to two real-world mortality datasets from the USA, involving deaths from tobacco- and alcohol-related cancers. Despite challenges such as zero values and small sample sizes, the estimator maintains superior performance. Overall, the proposed estimator offers an improvement in estimation performance for RSS, particularly in settings where auxiliary variables are available and memory type estimators are applicable.</p>