This study introduces new ridge estimators for the Liu-type logistic regression model which helps to improve the model performance if multicollinearity is present in the independent variables. Logistic regression is the regression that helps to model binary outcomes but it provides inaccurate and unstable regression coefficients in the presence of multicollinearity. As a result of this, the variance might increase and the predictive accuracy of the model gets reduced. To overcome this issue, the Liu-type logistic regression is used which uses ridge and Liu parameters to provide stable and accurate regression coefficients. Several ridge estimators are proposed in this study based on the Liu-type logistic model which can handle multicollinearity and give better predictive performance of the model. The proposed estimators have been tested on the demographic dataset from Urban Slums in Karnataka and through the empirical analysis it is observed that one among the new ridge estimators give the lowest Mean Square Error (MSE) when compared to the existing ridge estimators. The results show the usefulness of the new estimators to improve the performance of the model and also contribute to the betterment of the logistic regression techniques. This work highlights the critical need to handle multicollinearity in regression analysis and sets the path for researchers to further improve the estimators in the future.