Misclassification of binary covariates is pervasive in survival data, leading to inaccurate parameter estimates. Despite extensive research of misclassification error in Cox proportional hazards models, it has not been adequately researched in the context of accelerated failure time models. The log-logistic distribution plays an important role in evaluating non-monotonic hazards. However, the performance of misclassification correction methods has not been explored in such scenarios. We aim to fill this gap in the literature by investigating a method involving the simulation and extrapolation algorithm, to correct for misclassification error in log-logistic AFT models and later apply this method in real survival data.