<p>This paper presents neutrosophic exponential ratio-type estimators for deriving estimates of population parameters through two-phase sampling. The proposed estimators use neutrosophic auxiliary information as a means to handle the uncertainties as well as imprecise and ambiguous data characteristics that exist in real-world datasets. The implementation of neutrosophic framework leads to interval estimation procedures. The process of generating the estimations increases the robustness of parameter estimation by producing many highly reliable estimations. The suggested estimators’ bias and Mean Squared Error (MSE) are determined using a first-order approximation, after which the MSE is decreased via optimum characterizing constants.The suggested neutrosophic estimators exhibit a higher level of efficiency in comparison with traditional ratio-type estimators as measurable by the generation of smaller MSEs and tighter confidence intervals. Theoretical findings are verified. This research work employs authentic weather data from the Annual and Seasonal Rainfall (2016) report prepared by the Pakistan Meteorological Department (PMD) and data produced through the simulation using Neutrosophic Normal Distribution. The findings are corroborated by research based on observed rainfall data that was simulated using the Neutrosophic Normal Distribution and published by the Pakistan Meteorological Department (2016) Annual and Seasonal Rainfall. The findings suggest that these estimators may be applied in any number of environments that require accuracy and reliability of parameter estimate. The benefits of neutrosophic approaches are presented in the study. The survey science employing these sample approaches is enhanced by the two-phase sampling methods.</p>