Responding to inquiries within the legal field is notably challenging due to the complexity and variability of legal documents. Delivering precise responses to legal questions often requires domain-specific expertise, posing difficulties even for experienced professionals. The Question-Answering (QA) task, a subtask of Natural Language Processing (NLP), is designed to generate answers to natural language questions. In this study, we explore how QA systems can improve contract analysis by accurately extracting relevant clauses using advanced deep-learning models. By carefully creating a subset of the CUAD dataset, focusing only on relevant categories, we aimed to improve the accuracy of our models. We thoroughly tested several top transformer models to evaluate their performance in extracting important clauses from complex legal documents. Our findings show the great promise of these models in automating and enhancing the accuracy of legal document analysis.