It has been repeatedly shown that the ridge regression model is a desirable shrinking technique to lessen the consequences of multicollinearity. When the response variable involves binary data, the logistic regression model is a well-known model in use. However, it is well known that multicollinearity has a detrimental impact on the variance of the logistic regression coefficients' maximum likelihood estimate. Numerous scholars have suggested a logistic ridge estimator as a solution to this issue. The jackknifing logistic ridge estimator (JLRE) is suggested and derived in this study. The goal of the JLRE is to obtain a diagonal matrix with low diagonal element values, which will reduce the shrinkage parameter and enable a better, less biassed estimator to be produced. According on the results of our Monte Carlo simulation, the JLRE estimator can significantly outperform other available estimators. Additionally, the JLRE estimator surpasses the logistic ridge estimator and the maximum likelihood estimator in terms of predictive performance, according to the practical application findings.