A novel framework integrating Segregated Heuristic Chains and generative anomaly patterns has been developed to address the escalating complexities in detecting ransomware activities. The proposed methodology systematically isolates and analyzes behavioral anomalies across multiple heuristic layers, enhancing the granularity of detection mechanisms while maintaining computational efficiency. Through the application of unsupervised learning techniques, generative models construct anomaly patterns capable of identifying both known and previously unseen threats with high precision. Modular design ensures scalability and adaptability to diverse deployment environments, including enterprise networks, cloud platforms, and endpoint devices. Experimental evaluation demonstrated superior performance in detecting ransomware families such as LockBit, BlackCat, Hive, and Conti, with consistently low false positive rates and minimal resource consumption. The architecture leverages temporal and feature-based analysis to capture behavioral trends, providing actionable insights for proactive cybersecurity strategies. Anomaly score distributions revealed significant variability in the behavioral characteristics of different ransomware families, enabling targeted and efficient responses to specific threats. Comparative analysis with traditional approaches highlighted substantial improvements in detection accuracy and adaptability to novel ransomware variants. Resource utilization metrics confirmed the framework's suitability for realtime applications, ensuring consistent performance across varied operational contexts. Insights derived from feature importance analysis and clustering further emphasized the framework's ability to differentiate ransomware families based on distinct operational patterns. The integration of advanced learning mechanisms and heuristic stratification provides a transformative approach to ransomware defense, addressing critical gaps in current methodologies while offering scalability, efficiency, and robust detection capabilities. Through its innovative design and comprehensive evaluation, the framework establishes a significant step forward in enhancing the resilience of modern cybersecurity systems.