The increase in the creation of fake web pages by attackers is leading to a sharp increase in cyberattacks. Attackers use these fake web sites to advertise products to Internet users, distribute malicious programs, or steal users' valuable logins and passwords. Traditional solutions for detecting such fake web addresses are not effective in detecting newly created fake web addresses. In this article, we propose a new approach that combines several machine learning algorithms. Therefore, we use various selected features to improve the accuracy of sorting and classifying web pages. From our experimental results, it can be seen that using the proposed approach, the Random Forest (RF) classifier showed the best accuracy of 99%. It can be seen that the Random Forest classifier can be considered more reliable than the others in detecting fake web addresses.