<p>In survey sampling, accurate estimation of the population mean is the main problem; however, most existing estimators depend on the assumption that observations and auxiliary information are precise and determinate. This assumption is often violated in real-world surveys, where data are affected by ambiguity and uncertainty arising from incomplete information. Although neutrosophic statistics provides an effective framework for modeling such uncertainty, the development of efficient mean estimators under neutrosophic ranked set sampling (NRSS) remains limited. To address this gap, this paper proposes a novel and efficient class of neutrosophic estimators for the population mean estimation under NRSS. The proposed estimators integrate auxiliary information within a neutrosophic framework, allowing systematic incorporation of indeterminacy while enhancing the efficiency of estimation. The expressions for the neutrosophic bias and mean square error (MSE) are derived, and theoretical conditions for superiority over the existing neutrosophic estimators are established. The performance of the proposed estimators is evaluated through simulation study and real data applications. The results consistently demonstrate that the proposed neutrosophic estimators achieve lower MSE and higher relative efficiency (RE) than the competing estimators, particularly as the correlation between study and auxiliary variables increases. These findings confirm the practical usefulness of the proposed methodology for reliable mean estimation in surveys conducted under indeterminacy.</p>