Large language models (LLMs) have shown strong reasoning abilities in mathematical reasoning, commonsense reasoning, and code generation. However, negotiation requires interactive reasoning, where each action can influence the opponent’s future behavior. Agents must therefore look beyond the current dialogue state and consider the long-term effects of their actions. Existing LLM-based negotiation agents often rely on prompting or fine-tuning from self-play trajectories. These methods can produce fluent responses and help models learn useful bargaining patterns. Yet at decision time, they often remain reactive, responding mainly to the current dialogue without reasoning about how different actions may shape future negotiation paths. We introduce an online strategic reasoning method that combines Monte Carlo Tree Search (MCTS)-based lookahead planning with opponent simulation. Before responding, our agent uses a proxy opponent agent to simulate future interactions and evaluate candidate strategies by their expected long-term outcomes. To make opponent simulation more precise, we proposed a Theory-of-Mind-inspired opponent modeling module that infers latent opponent attributes from dialogue history and conditions the proxy opponent agent during search. Experiments on CraigslistBargain and A2A-NT show that our method improves negotiation efficiency and profitability.