The binary logistic regression (BLR) model is used as an alternative to the commonly used linear regression model when the response variable is binary. As in the linear regression model, there can be a relationship between the predictor variables in a BLR, especially when they are continuous, thus giving rise to the problem of multicollinearity. The efficiency of maximum likelihood estimator (MLE) is low in estimating the parameters of BLR when there is multicollinearity alternatively, the ridge estimator (RR), the Liu estimator (LE), the Liu-type estimator (LTE) and The Modified Ridge-Type estimator (MRTE) were developed to replace MLE. However, in this study, we compared all estimators by the mean squares errors (MSE) to get the best estimator that mitigates the effect of multicollinearity. Finally, a simulation study was conducted to illustrate the theoretical results. The result shows that the modified Ridge type estimator outperforms all other estimators followed by Liu estimator.