This paper outlines a holistic framework for improving query comprehension in information retrieval (IR) systems through deep contextual embeddings. Using Sentence-BERT, we transform the queries and documents into dense semantic representations and then calculate cosine similarity for the relevance measure. We especially emphasize interpretability by adding various statistical and visualization techniques, like heatmaps, KDE plots, UMAP projection, dendrograms, and box plots, to identify and analyze latent semantic patterns and keep close tabs on the inner workings of how different documents relate to such varied query formulations. We also follow an avenue of the interquartile dispersion and distributional behavior of similarity scores to understand the embedding consistency and discriminative power. This methodology, therefore, encompasses both retrieval accuracy and explainability as it provides visible insights into improving query reformulation, semantic search, and content recommendation. Experimental visualizations have further demonstrated their effectiveness towards deep semantic alignment, especially during the processing of complex multi-topic corpora. The abstract is technically solid and easily comprehensible because it is rationally structured and clearly states the research’s methodology, instruments, and objectives.