Current chronic kidney disease (CKD) management relies on fixed clinical thresholds and static evaluation methods that fail to capture individual patient characteristics and dynamic disease progression. Most existing machine learning applications operate as “black box” systems without adequate clinical context or temporal analysis, limiting their clinical utility. This research presents a comprehensive AI framework that integrates clinical guideline-based classification, specialized survival analysis, and explainable AI techniques. The framework introduces two key innovations: First, a domain-specific survival analysis approach that separates kidney function into renal, electrolyte, and metabolic components, enabling targeted analysis of distinct disease processes. Second, an advanced explainable AI (XAI) framework that moves beyond conventional methods by integrating dynamic attention mechanisms with causal, graph-based reasoning to produce clinically actionable insights. The integrated approach generates accurate CKD staging, progression risk assessment with clinical timelines, and transparent explanations that bridge advanced computational predictions with practical clinical decision-making. Validation on 400 patients with comprehensive temporal data demonstrates superior predictive performance while maintaining clinical interpretability essential for real-world healthcare deployment. The framework enables precise patient risk stratification and provides actionable decision support for healthcare providers.