Enhancing Quranic Ethics and Morality: An NLP- Based Semantic Search Model for Urdu Translation

he Quran offers unparalleled guidance on ethics and morality, but extracting relevant teachings from its Urdu translations remains a challenge due to conventional keyword-based search methods that lack contextual understanding. This research proposes a Natural Language Processing (NLP)--based query model designed to improve the retrieval of Quranic verses related to ethics and morality in Urdu translations. By integrating Sentence Transformers for semantic search and a custom synonym expansion module, the model enhances accuracy and relevance in retrieving verses. The dataset widely accepted Urdu translation of the Quran, and the system is evaluated using precision, recall,and relevance scoring metrics to ensure effectiveness. The study demonstrates how NLP techniques can bridge the gap between traditional Quranic studies and modern computational methods, providing scholars, educators, and researchers with an advanced tool for exploring Quranic ethics. The proposed system achieves high precision and recall, offering a more effective approach to Quranic verse retrieval compared to conventional keyword-based searches. The research also highlights future opportunities for expanding the model to support multiple languages and broader thematic searches, further enhancing accessibility to Quranic knowledge.