As artificial intelligence (AI) and gamification continue to transform education, innovative frameworks are needed to create more engaging, adaptive, and effective learning experiences. This paper introduces a structured three-phase adaptive framework that leverages AI-driven techniques and gamified mechanisms to enhance machine learning (ML) education. The first phase assesses individual student performance through timed quizzes, dynamically classifying learners based on their accuracy and response time. In the second phase, AI-powered decision-tree algorithms form balanced teams, fostering collaborative problem-solving while maintaining a competitive dynamic. The final phase introduces AI-adjusted strategic challenges, such as real-time betting on question difficulty, to develop risk management skills and reinforce conceptual mastery. By integrating leaderboards, adaptive question difficulty, and gamified incentives, the framework transforms traditional ML education into a personalized, interactive, and student-centered experience. This framework provides a scalable and data-driven foundation for future empirical studies on student motivation, conceptual understanding, and learning outcomes in adaptive gamified environments.