The escalating sophistication of ransomware attacks necessitates the development of advanced detection methodologies capable of identifying complex and evolving threat patterns. The Hierarchical Behavioral Pattern Analysis (HBPA) framework introduces a novel approach that models ransomware behaviors through a hierarchical structure, capturing intricate behavioral sequences and dependencies. By integrating probabilistic models and calculus-based representations, HBPA achieves a detection rate of 98.7% with a false positive rate of 1.3%, outperforming traditional detection systems. The framework's adaptability to emerging ransomware variants and scalability across varying dataset sizes demonstrate its potential to enhance cybersecurity defenses. Comprehensive experimental validation affirms HBPA's efficacy in identifying both known and novel ransomware threats, positioning it as a significant advancement in the field of automated threat detection.