This paper delves into the cutting-edge domain of decision optimization within multi-agent systems, leveraging the prowess of Large Language Models (LLMs), particularly GPT-4. We explore the integration of LLMs in multi-agent frameworks to optimize decisions, a step beyond traditional decision-making processes. The study showcases how LLMs can process extensive datasets, extract nuanced insights, and suggest optimal solutions, significantly enhancing decision accuracy and efficiency in complex multi-agent environments. Our methodology encompasses the integration of LLMs into multi-agent systems, experimental validation of this approach, and an in-depth analysis of the impact of LLM-driven decision optimization in varied application scenarios. The article proposes a general framework where the integration of GPT-4 in multi-agent decision optimization processes leads to marked improvements across various scenarios.