Early identification of mathematics learning difficulties is critical for improving student outcomes and reducing long-term academic disparities. Traditional assessment practices often rely on summative evaluations that provide delayed and limited diagnostic insight into learners’ evolving skill profiles. This review paper examines the development and application of predictive intervention models that leverage reliable classroom assessment data trends to identify mathematics skill gaps at an early stage. Emphasis is placed on formative and continuous assessment data—including quizzes, homework performance, concept mastery checks, and longitudinal progress indicators—as inputs for predictive analytics frameworks. The paper synthesizes existing literature on data-driven educational modeling, learning analytics, and intervention design to evaluate how statistical methods, machine learning algorithms, and trend-based analytics can forecast learner difficulties across core mathematical domains such as numeracy, algebra, geometry, and problem solving. Furthermore, the review explores the integration of predictive outputs with targeted instructional interventions, adaptive learning pathways, and differentiated teaching strategies. Key challenges related to data reliability, assessment validity, model interpretability, and ethical considerations in student data use are critically examined. By consolidating empirical evidence and methodological approaches, this paper provides a comprehensive foundation for educators, policymakers, and researchers seeking to implement proactive, evidence-based intervention systems in mathematics education. The findings highlight the potential of predictive intervention models to transform classroom assessment data into actionable insights that support timely, personalized, and equitable mathematics instruction.