<p>This article presents two novel goodness-of-fit tests for the Rayleigh distribution, specifically designed for analyzing Type-II censored data. The development of these tests is based on the Kullback–Leibler information criterion. Both tests exhibit consistency, and one of the test statistics possesses a nonnegative property akin to the Kullback–Leibler information. To assess the performance of the proposed tests, a comprehensive simulation study is conducted. The simulation results provide percentile points and power values, offering valuable insights into the effectiveness of the tests in detecting departures from the Rayleigh distribution. Furthermore, a real-life data analysis is included to demonstrate the practical application of the proposed tests. This application showcases how these tests can be utilized to assess the goodness-of-fit of the Rayleigh distribution when analyzing real-world data.</p>