Accelerated life testing provides an effective approach for assessing product reliability within a shorter time by exposing test units to higher levels of stress than those experienced under normal operating conditions. This study considers a constant-stress testing plan, in which each product is subjected to a fixed level of stress throughout the entire experiment. The lifetime of the product under such a condition is assumed to follow the inverse Chen distribution. The unknown parameters of this distribution are estimated using two classical estimation methods: the maximum likelihood method and the maximum product of spacings method. Approximate confidence intervals for the estimated parameters are also constructed. Furthermore, the reliability function and the hazard rate function at normal operating conditions are estimated by linking the scale parameter to the stress level through a log-linear relationship. To evaluate and compare the performance of the two estimation approaches, a comprehensive Monte Carlo simulation study is conducted along with the analysis of two real datasets from engineering applications. The findings demonstrate the suitability of the inverse Chen model in describing lifetime data and highlight the relative efficiency and robustness of the two estimation methods under various conditions.