Solving complex numerical and theoretical problems in domain-specific areas remains a challenge for current large language models (LLMs), as they often fail to apply mathematical reasoning effectively within these domains. Existing approaches to enhance LLMs’ capabilities for solving domain-specific problems include taxonomy-based approaches, reinforcement learning for practical applications, and methods like forward-backward reasoning for answer verification. In this paper, we propose a structured LLM training methodology to optimize the models for domain-specific reasoning, focusing on both numerical and theoretical problems. Our proposed system defines a flow that includes the generation of a custom domain-oriented dataset, using which rigorous training is conducted on the baseline models. The result analysis of the study is based on several metrics including BLEU, ROUGE, mean squared error (MSE), and mean absolute percentage error (MAPE), which indicates that the models created using the proposed system have a superior performance compared to other models. Our findings reveal that these enhanced LLMs can provide precise and efficient solutions for domain-based queries, thereby advancing their practical utility in real-world applications.