Artificial Intelligence (AI) is advancing toward traditionally theologian-assigned functions, but its suitability for sacred interpretation is still under debate. This research investigates the developing uses of AI in scriptural exegesis, doctrinal reasoning, and preservation of heritage to explicate the corresponding benefits and risks. The paper surveys the technical literature for natural-language processing, machine-learning model-based pattern finding, and knowledge-graph reasoning techniques, thus catalogs twenty active systems ranging from the Bible to the Talmud to Buddhist texts, and it surveys the theological, ethical, and cultural discourse pertaining to each. The study demonstrates how transformer-based natural language processing extends multilingual access and excavates thematic relations, while unsupervised methods uncover diachronic changes, and chatbots enabled by AI democratize access. However, all tools under survey share data bias, reduce contextuality, and hide reasoning, thus undermining entrenched perceptions of authority and authenticity. Stakeholder reviews disclose inadequate representation of minor traditions and insufficient explainability protections. It prescribes design principles, including theological diversity, human supervision, and user education, to counteract these limitations. In conclusion, the study asserts that AI is capable of responsibly complementing, but replacing, human understanding under circumstances where the system is co-developed with scholars, source and limitations are transparently logged, and consequential judgment is reserved for validated religious experts. Future development requires interdisciplinary collaboration and creation of openly accessible datasets to preserve both technological innovation and doctrine integrity. Future studies are to test these principles in live practice settings.