It is known that multicollinearity not only leads to the generation of redundant data as a result of data repetition, but also affects the stability of linear models of artificial intelligence and the reliability of results. The negative effects of multicollinearity can be seen especially clearly in the development of mathematical models of artificial intelligence algorithms. That is, the coefficients will be unstable in a mathematical model developed on the basis of a data set with multicollinearity. As a result of it, misconceptions arise in scientific conclusions drawn based on the coefficients. This article first discusses multicollinearity and its negative consequences in detail. In addition to, methods for determining multicollinearity in a data set based on the correlation coefficient, the variance inflation coefficient, and the condition index are discussed in detail. Moreover, this research paper analyzes the methods of eliminating multicollinearity by removing, combining features, and Principal Component Analysis. At the same time, the research will investigate the impact of multicollinearity on machine learning models such as LogisticRegression, LinearRegression, LinearSVC, and XGBClassifier using a multicollinearity dataset. The results of the study showed that eliminating multicollinearity leads to an increase in the accuracy of all considered artificial intelligence models. In particular, the ROC value increased by 0.102 in the Logistic Regression model, by 0.129 in the Ridge Classifier, and by 0.121 in the Linear SVC. Although the smallest difference value of 0.094 was achieved in the XGBoost model, the accuracy was higher than that of the other models. After the experimental results, the article presents conclusions and recommendations based on the results obtained.