Online question-and-answer (Q&A) repositories have become vital resources for knowledge acquisition. In specialized domains such as Islamic jurisprudence, these repositories present additional complexity: short, concise questions are paired with lengthy, context-dependent answers written in highly domain-specific language. This structure makes automatic and interpretable topic discovery both crucial and challenging. Conventional topic modeling and recent embedding-based clustering approaches have improved semantic representation, yet they continue to suffer from cluster instability, poor label quality, and a lack of reproducible evaluation standards, limiting their practical reliability and interpretability. To overcome these issues, this study introduces HybriDToD, a hybrid deep topic discovery framework that combines the scalability of unsupervised learning with the semantic precision of Large Language Models (LLMs). The core innovation lies in integrating two novel modules, Topic Auditor and Interactor, that together establish a feedback-driven evaluation loop. The Topic Auditor employs an LLM-as-a-Judge mechanism to assess topic quality across six rubrics, providing structured, human-like evaluation without extensive manual effort, while the Interactor incorporates minimal expert feedback to refine and validate the automatically generated topics. Experiments on the ISLAMQA dataset demonstrate that HybriDToD achieves superior coherence, interpretability, and stability. This research contributes a scalable and explainable framework for organizing and analyzing large religious or educational Q&A archives, advancing automated topic modeling toward greater transparency, accountability, and domain adaptability.