Large Language Models (LLMs) can answer diverse questions but often generate factually incorrect responses. SemEval-2025 Task 8 focuses on table-based question-answering, providing 65 real-world tabular datasets and 1,300 questions that require precise filtering and summarization of underlying tables.We approach this problem as a neuro-symbolic code generation task, translating natural language queries into executable Python code to ensure contextually relevant and factually accurate answers. We formulate LLM decoding as a Markov Decision Process, enabling Monte Carlo Tree Search (MCTS) as a lookahead-based planning algorithm while decoding from the underlying code-generating LLM, instead of standard beam search.Execution success on synthetic tests and real datasets serves as a reward signal, allowing MCTS to explore multiple code-generation paths, validate outcomes, assign value to partial solutions, and refine code iteratively rather than merely maximizing sequence likelihood in a single step. Our approach improves accuracy by 2.38x compared to standard decoding.