The integration of Artificial Intelligence (AI) into educational frameworks has initiated a paradigm shift in pedagogical methodologies, particularly within the domain of mathematics. Mathematics education traditionally suffers from standardized approaches that fail to address heterogeneous student learning paces, cognitive styles, and localized conceptual gaps. This article explores the application of modern pedagogical technologies driven by AI, including Intelligent Tutoring Systems (ITS), adaptive assessment platforms, and predictive analytics. Drawing on empirical data from global implementations, the study examines how AI optimizes customized learning trajectories, provides instantaneous diagnostic feedback, and alleviates cognitive load. The findings indicate that AI-driven pedagogical tools improve mathematical proficiency by 15% to 25% compared to conventional instructional methods, while simultaneously optimizing the time allocation of educators. The methodology utilizes a comprehensive review of recent educational experiments, quantitative data synthesis, and comparative analysis of adaptive algorithms. The article concludes with a discussion on structural challenges, including algorithmic bias, data privacy, and the evolving role of the educator in an AI-mediated classroom.