The article introduces a new method called Bisquare Weighted Ridge Least Median Squares Regression (WRLMS) to deal with multicollinearity and outliers in linear regression. By combining principles from ridge regression and the Bisquare weighted function, this method helps detect and handle these issues more effectively. The use of robust regression techniques like Least Median Squares (LMS) improves trend estimations and outlier detection. Through a medical application, it is shown that WRLMS outperforms other estimators in terms of efficiency. The study demonstrates that WRLMS is better at estimating parameters in the presence of multicollinearity and outliers.