Modern cybersecurity demands innovative approaches capable of addressing the rapidly evolving nature of sophisticated threats targeting critical systems. Temporal Entropy Fingerprinting introduces a novel framework for detecting malicious activities by leveraging the dynamic properties of entropy patterns over time. Through the systematic analysis of entropy fluctuations, the approach identifies behaviors associated with encryption, file manipulation, and anomalous system activity, key characteristics of ransomware attacks. The framework achieves high detection rates by integrating probabilistic models, dynamic thresholding, and advanced segmentation techniques to quantify anomalies across varying operational contexts. Experiments conducted with datasets encompassing contemporary ransomware variants, such as LockBit and BlackMatter, demonstrated consistent performance across diverse scenarios, ensuring both accuracy and scalability. The entropy-based architecture supports real-time anomaly detection while maintaining computational efficiency, addressing the limitations observed in traditional static and dynamic analysis methods. By enabling a granular understanding of entropy distributions during critical ransomware operations, the framework provides actionable insights that enhance situational awareness. Furthermore, modular design principles facilitate seamless integration with existing cybersecurity infrastructures, ensuring adaptability to resource-intensive environments. The results indicate significant improvements in detection precision and recall, particularly in identifying previously unseen ransomware families. Comparative analysis against baseline methods highlighted the framework's capability to maintain low false positive rates even under highvolume data processing conditions. With entropy metrics serving as a foundation, the proposed approach offers a resilient and effective solution for mitigating modern ransomware threats.