This paper presents a novel approach for identifying the reciter, sura, and verse of a given Quranic passage using pre-trained embedding models and transfer learning. Our approach involves training a deep learning model on a large Quranic recitation audio recordings dataset and using the resulting embeddings to compare and classify different reciters. We also present a workflow for identifying the specific sura and verse of a Quranic passage using a speech-to-text model and elastic-search for the query. We evaluate our approach using a variety of metrics and demonstrate its effectiveness in accurately identifying the reciter, sura, and verse of a given passage. We discuss the potential applications of this approach in the fields of Quranic studies and Islamic education and outline directions for future work in this area.