This review paper provides a comprehensive survey of Large Language Models (LLMs) applied to Document-Based Question Answering (DBQA). The rapid advancements in LLMs have transformed DBQA systems by enhancing their capabilities in complex reasoning, multi-hop question answering, and contextual understanding. We explore the historical evolution of QA systems before the introduction of LLMs, followed by an in-depth analysis of datasets, benchmarks, and evaluation metrics that underpin DBQA performance. A key contribution of this work is the categorization of LLMs that can perform DBQA based on linguistic focus, distinguishing between monolingual models tailored for specific languages and multilingual models designed to operate across diverse linguistic contexts. Furthermore, we examine notable LLM architectures, their domain-specific adaptations in areas like healthcare and law, and their performance in handling both structured and unstructured data.