The purpose of this study is to structurally analyze how big data accumulated in educational environments with the development of information and communication technologies can be integrated into mathematics education through the disciplines of Educational Data Mining (EDM) and Learning Analytics, and to discuss the multi-layered contributions to educational processes and the accompanying ethical risks by concretizing the algorithmic applications in the literature. The study is a qualitative conceptual analysis and a comprehensive literature review. In this direction, students' interaction logs, performance outputs, and affective data in digital environments are analyzed by associating them with the theoretical frameworks of Hiebert and Lefevre's (1986) "Conceptual and Procedural Knowledge," Schoenfeld's (1992) "Mathematical Thinking," Zimmerman's (2002) "Self-Regulated Learning," and Vygotsky's "Zone of Proximal Development" (ZPD). As a result of the literature analysis, it has been determined that data mining applications in mathematics education have gained momentum primarily in three main axes: performance prediction, misconception detection, and adaptive feedback. The pedagogical equivalents of supervised and unsupervised machine learning techniques used in this process are classified accordingly. Furthermore, it has been established that students' moments of boredom or frustration can be detected instantly in game-based environments, and student mastery can be probabilistically mapped using Bayesian and Deep Knowledge Tracing models. Consequently, it has been observed that Educational Data Mining techniques transform the traditional and outcome-oriented assessment approach into a "process-oriented and formative assessment" culture. In this context, it is recommended that future systems should be designed in accordance with the principles of Explainable Artificial Intelligence (XAI), affective dimensions should be included in the process through Multimodal Learning Analytics (MMLA), and a collaborative "precision education" model that combines the analytical power of artificial intelligence with the pedagogical content knowledge of the teacher should be adopted.