Semantic sentence similarity measures the degree of resemblance between multiple sentences. This similarity is a foundational element in information retrieval, machine translation, etc. This paper focuses on natural language processing techniques to analyze the semantic similarity in Hadiths, which are significant religious texts in Islam. Our objective is to investigate the extent of semantiv overlap between Hadiths across various topics, with the aim to provide insights into the cohesion and interconnectedness of Hadiths. We use AraVec and GPT embeddings to represent Hadiths numerically, followed by UMAP (Uniform Manifold Approximation & Projection) to project these embeddings to 2D. The projection serves to visually interpret the relationships between Hadiths, facilitating a deeper understanding of content and semantic interrelations. Our results unveil semantic clusters and connections within Hadiths, contributing to the exploration of Islamic textual heritage through modern computational methodologies. This study suggests that GPT outperforms AraVec, providing a more advanced representation that discerns intricate semantic relationships and subtle nuances within the Hadiths.