This paper proposes an improved class of estimators for finite population mean under simple random sampling without replacement using an auxiliary variable to enhance efficiency. The proposed class of estimators integrates flexible parameterization and transformed auxiliary variable, allowing it to be adapted to existing estimators and novel estimators through specified parameter choices. Theoretical analysis derives the bias and mean squared error of the proposed class of estimators up to the first degree of approximation, demonstrating its superiority over existing estimators discussed in the paper under certain conditions. Numerical experiments on real-world data sets and simulated populations validate the enhanced performance of the proposed estimators.