<p>This paper introduces the Log–Exponential Fréchet (LEF) distribution as a flexible generator-based extension of the classical Fréchet model for modeling right-skewed and heavy-tailed data. The proposed model enhances tail adaptability while preserving the fundamental characteristics of the Fréchet baseline. Parameter estimation is developed under the balanced ranked set sampling (BRSS) framework, which improves sampling efficiency in situations where measurements are costly or difficult to obtain. A unified inferential framework is established to accommodate several estimation approaches, including maximum likelihood, least squares, maximum product of spacings, Cramér–von Mises, Anderson–Darling, minimum spacing distance, and Linex-based spacing estimators. The framework is based a transformed-uniform representations, allowing the derivation of unified objective functions and associated gradient expressions. The performance of the estimators is evaluated through an extensive Monte Carlo simulation study under different sample sizes. The results indicate that estimation accuracy improves as the sample size increases, with the Cramér–von Mises and Anderson–Darling estimators demonstrating superior stability and accuracy. The practical applicability of the proposed model is illustrated using two real datasets, supported by goodness-of-fit comparisons with competing distributions. Overall, the proposed LEF distribution and the associated inferential framework provide an effective and flexible approach for modeling and analyzing heavy-tailed data in reliability and engineering applications.</p>