In this article, we introduce a regression model tailored for fitting binary data affected by misclassification in the response variable and Berkson-type measurement error in the covariate. The conventional assumption of a normal distribution for measurement error may inadequately represent atypical observations present in the dataset. To address this limitation, our model incorporates misclassification in the response variable and Berksontype measurement error, employing the Student-t distribution for more robust modeling of these atypical observations. We utilize the cumulative distribution function from the Student-t distribution as the link function, enhancing our ability to capture the dataset’s unique characteristics. Model parameters are estimated via the maximum likelihood method. We conduct a comprehensive Monte Carlo simulation study to thoroughly assess the impact of measure...