There is a vast quantity of information contained within the billions of web pages that make up the World Wide Web (WWW). Search engines carry out a variety of activities depending on their own architectures for retrieving the necessary information from the WWW. The search engine typically returns a huge number of pages in response to a user's query when the user submits one. Numerous ranking techniques have been utilized on search results to aid consumers in navigating the result list. The majority of ranking algorithms described in literature are either content-based or link-based, and they do not take user usage patterns into account. The presented study discusses web mining ranking algorithms depending on structures, contents, and usages and suggests a new ranking method to assess the significance of links with the use of semantic metadata analysis, which considers the number of links visited across nearly all regions, time periods, and related topics and queries. Furthermore, the suggested system uses the user's query to find more relevant information. The most valuable pages are thus displayed at the top of the result list based on user browsing behavior, which significantly decreases the search space. Results showed better ranking output based on different criteria, such as the number of links visited yearly, the number of links visited hourly, the number of links visited by region, the number of links visited by related topics, and the number of links visited by related queries. [ABSTRACT FROM AUTHOR]