In the presence of indeterminate, inconsistent, or imprecise data, the conventional estimation procedures often fail to give reliable results. To address this limitation, the present study develops novel neutrosophic estimation procedures for the population mean by incorporating auxiliary attribute information under simple random sampling (SRS). The proposed approach extends the conventional attribute-based estimation procedures to the neutrosophic framework, where uncertainty, truthiness, and falsity are simultaneously represented. We adapted some prominent conventional estimators under neutrosophic environment and developed a new class of neutrosophic estimators with their properties, such as bias and mean square error (MSE) up to first-order approximation. Comparative efficiency analyses are conducted with respect to the adapted estimators to highlight the gain in precision achieved through the proposed estimators. A comprehensive simulation study, along with an empirical illustration on real-world data, demonstrates that the developed neutrosophic estimators consistently outperform the adapted neutrosophic estimators. The developed framework thus provides a robust alternative for mean estimation when uncertain or imprecise attribute information is available.