Web search engines offer pertinent documents in response to user inquiries. Additionally, the user may not need all of the redundant information found in these result-set papers. To find pertinent information, the user must make an effort to navigate each document. Web object search engines were suggested as a solution to get around such unpleasant overheads. These systems offer strong vertical search capabilities, ensuring that the query result set only includes pertinent Web object data. While numerous methods have been put out to geographically tag articles for online search engines, less focus has been placed on geographic labeling for web objects. The process of assigning geographic labels is made more difficult by the noise that exists in the Web objects as a result of faulty object extraction. in the literature as of late. Even with the proposed machine learning classification technique, there is still much room for improvement in terms of labeling accuracy for geographically labeled Web objects. In this work, geographic categorization of Web objects is achieved through the use of classifier. When compared to the state-of-the-art method, the suggested method offers at least 20% higher labeling accuracy and twice as much computational efficiency.