The article introduces new ways to estimate population variance using two auxiliary variables, even when the mean of one variable is missing. The researchers developed estimators and calculated their bias and error. They found conditions where their estimators are more efficient than existing ones. Through studies, they showed that their estimators perform well compared to others.