A lower record sample is utilized to derive E-Bayesian (EB) estimates for the rate parameter of the inverse Weibull distribution. These estimates are developed under two different error loss functions: the scaled squared error loss (SSE) function and the linear exponential error loss (LINEX) function. The expected mean squared errors (E-MSEs) of these EB estimates are computed in order to evaluate the accuracy and dependability of these estimates. An exhaustive Monte Carlo simulation research is carried out in order to carry out a detailed comparison of the performance of different estimators. This simulation can be used to better understand how the estimators behave and how resilient they are under different scenarios and sample sizes. The analysis of two real-world data sets offers a further illustration of how the presented approaches can be used in practice. These examples further validate the usefulness of the EB estimates in statistical inference and decision-making processes by demonstrating how well they simulate real-life data.