The escalating threat of malicious software necessitates innovative detection methodologies to protect critical digital infrastructures. The Contextual Anomaly Graph Analysis (CAGA) framework emerges as a novel approach, leveraging graph-based anomaly detection to identify ransomware activities within complex network environments. By constructing detailed graphs that represent system behaviors, CAGA captures contextual relationships often overlooked by traditional detection systems. The framework's architecture integrates advanced algorithms for graph construction, feature extraction, and anomaly detection, enabling the identification of subtle deviations indicative of ransomware presence. Comprehensive evaluations demonstrate CAGA's high detection accuracy across diverse ransomware variants, including LockBit, BlackCat, and Hive, with minimal false positive rates when tested against benign applications. The framework exhibits scalability, maintaining efficient processing times across varying network sizes, and operates effectively within constrained computational resources. Real-time processing capabilities are achieved through integration with streaming platforms, ensuring prompt detection and mitigation of ransomware threats. Comparative analyses reveal that CAGA outperforms traditional signature-based detection methods, particularly in identifying novel ransomware behaviors. While the framework shows promise, considerations regarding detection latency and performance in diverse real-world scenarios are discussed, highlighting areas for future enhancement. The CAGA framework represents a significant advancement in cybersecurity, offering a robust tool for proactive ransomware detection and contributing to the fortification of digital defenses.