Many internet users nowadays want search engines that are capable of quickly searching webpages and obtaining data. Traditional web search engines, on the other hand, confront significant difficulties in retrieving reliable results in the shortest time possible. Traditional search engines must additionally broaden conflicting queries depending on the semantic relationship between keywords. As a result of the research, a one-of-a-kind model for ranking online sites has been developed, which employs a semantic web page retrieval technique for finding significant results of queries that are unclear utilising semantic relations. The article provides a new model for ranking web pages that classifies meaningful search results using a semantic web page retrieval method. In experiments, the proposed model is compared to four different input circumstances. The resulting findings are compared to other web page ranking algorithms, such as real-time search engines. The obtained results are compared and analysed to show that the scheme has improved.