<![CDATA[This study aims to group thematically translated texts of Indonesian Quranic verses using a Word2Vec-based machine learning approach and the KMeans Clustering algorithm. The process begins with text preprocessing, creating vector representations using Word2Vec, and then clustering using KMeans with quality evaluation using the Silhouette Score metric. The experimental results show that the model is able to form six main thematic clusters that semantically describe themes such as prayer and hope, moral evil, social law, the teachings of revelation, divinity, and the stories of figures and ethics. Two-dimensional visualization with PCA strengthens the interpretation of the formed clustering patterns. This study proves that the unsupervised learning approach can be relied upon to support the automation of digital thematic interpretation objectively and systematically. In addition, the results of this clustering have the potential to become the basis for the development of topic-based verse search systems, contextual Quranic learning applications, and technology-based exploration of Islamic studies. This study also supports the achievement of Sustainable Development Goals (SDGs) point 4 regarding increasing access to inclusive and quality education through information technology.]]>
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