This paper addresses the persistent problem of vocabulary mismatch in information retrieval, a field that aims to locate and retrieve relevant information from a database. The paper introduces the concept of query expansion, a technique that enhances the performance of retrieval models by expanding queries with semantically related terms. The primary goal of this research is to improve the query expansion using Asymmetric Kullback-Leibler Divergence from the randomness framework. This work also presents a comprehensive experimental analysis, implementing both classic and state-of-the-art algorithms like neural reranking models, i.e., Kernel Neural Ranking Model and Vanilla-Bert on two different large real-world datasets. The results demonstrate significant improvements in retrieval performance when using the proposed KL divergence method.