This study introduces a novel family of estimators for the finite population mean that demonstrate improved accuracy by utilizing dual auxiliary information in simple random sampling. The proposed estimators were evaluated by deriving their bias and Mean Square Error (MSE) expressions up to the first-order approximation. The analysis identifies the conditions under which these estimators outperform existing methods. Real-world data were used to compute the MSE and Percentage Relative Efficiency (PRE) of the proposed estimators. Comparative results show that, under specified conditions, the new estimator family achieve greater precision, reducing MSE and enhancing estimation accuracy.