This paper aims to introduce a modification of the ridge estimator based on the singular value decomposition (SVD) technique of the design matrix (X ) to combat multicollinearity in the binary logistic model. This estimator is called a modified ridge logistic based on SVD estimator which is denoted as (MRLSVDE). We study the statistical properties of the proposed estimator in the context of the bias, variance-covariance matrix, and mean squared error. A Monte Carlo simulation study is conducted to evaluate the performance of the proposed estimator over the ridge logistic estimator (RLE) and the maximum likelihood estimator (MLE) based on the scalar mean squared error (SMSE) criterion. Moreover, an empirical application is provided to investigate the potential benefits of the proposed estimator in real-life fields. The results of the simulation study and real data application indicate that the proposed estimator outperforms the maximum likelihood and ridge logistic estimators in the scalar mean squared error sense.