<p>In the realm of sample survey research, the classical statistics approach primarily deals with precise and definitive types of data to estimate population parameters when additional information is available. However, this approach fails when faced with data indeterminacies. To address such ambiguities, neutrosophic statistics emerges as an extension of both fuzzy and classical statistics. Thus, in light of the challenges posed by indeterminacy in sampling, we have introduced a proficient neutrosophic class of estimators, with and without Searls technique (optimization tool) for estimating the mean utilizing additional (ancillary) information under neutrosophic simple random sampling (NeSRS). The expression for the Bias and mean square error (MSE) of propounded estimators are derived up to the first order of approximation. The primary objective of this manuscript is to attain optimal estimates using our proposed neutrosophic estimators for unknown population mean values, while minimizing MSE and maximizing relative efficiency (RE). We accomplish this through an extensive study utilizing neutrosophic real data and simulations.</p>