The convergence of artificial intelligence (AI) and game theory has opened up transformative possibilities regarding how systems adapt and interact in dynamic strategic environments. This paper presents a comprehensive summary of current developments where deep learning, reinforcement learning, and optimization algorithms have redefined traditional game-theoretic models. AI has now enabled adaptive decision-making in both competitive and cooperative scenarios, such as cybersecurity and autonomous driving, by combining knowledge from neural-network-based strategy formulation and multi-agent reinforcement learning. Special focus is employed with regard to the implementation of deep Q-networks and Q-learning, which allow agents to create ideal strategies through iterative self-learning in uncertain and complex situations. This study also highlights the ethical concerns, transparency, and bias present in strategies created by AI. By conducting algorithmic evaluations using tools such as TensorFlow and OpenAI Gym, the practical feasibility of these methods has been proven in various game scenarios. Despite challenges in scalability, interpretability, and incomplete information, the results confirm that AI methods not only enhance strategic modeling but also push the boundaries of what autonomous systems can achieve in interactive decision-making. This paper aims to contribute a combined understanding of how AI is revolutionizing game theory and outlines future research directions involving hybrid learning models, self-organizing systems, and quantum strategies, for more intelligent, ethical, and effective game-based decision systems.