Content toxicity has become a pressing issue in the deployment of artificial intelligence systems that interact with users through text, necessitating robust strategies to mitigate the generation of harmful language. Employing game-theoretic frameworks to address this challenge presents a novel and significant approach, as it allows for the strategic modeling of interactions between AI systems and adversarial entities, leading to the development of adaptive and resilient response mechanisms. The study demonstrates that conceptualizing the generation of toxic content as a strategic game, involving an AI language model and an adversarial prompt generator, can effectively reduce the occurrence of harmful outputs. Through the implementation of Nash equilibrium and other equilibrium concepts, the research illustrates how AI can be guided towards stable, non-toxic behavior, even when confronted with sophisticated adversarial tactics. Experiments conducted under various scenarios revealed that AI systems equipped with gametheoretic strategies are capable of dynamically adjusting to new threats, maintaining ethical standards in real-time interactions. The integration of continuous feedback mechanisms further enhances the system's ability to learn from past interactions, refining its strategies to minimize the likelihood of generating offensive or inappropriate language. This innovative approach not only addresses the immediate concerns of content toxicity but also offers a framework that can be extended to other domains requiring ethical and safe AI interactions.