The escalating frequency and sophistication of cyber threats means the need of the development of more effective detection mechanisms. The Dynamic Entropy Analysis (DEA) framework introduces a novel approach to ransomware detection through real-time monitoring of entropy variations within system files and processes, enabling the identification of malicious activities with high accuracy. Comprehensive evaluation of the DEA framework demonstrated a detection accuracy of 94.5%, surpassing traditional signature-based and behavior-based methods. The framework's adaptability to emerging ransomware variants was evidenced by a consistent detection rate exceeding 90% over a six-month period. Scalability assessments revealed effective operation across networks ranging from 50 to 500 devices, with minimal impact on system performance, as indicated by consistent detection accuracy and acceptable resource utilization metrics. Detection latency analysis indicated prompt identification of ransomware activities, with detection occurring within 0.5 to 2.5 seconds across various samples. The modular design of the DEA framework facilitates seamless integration with existing security infrastructures, enhancing its feasibility for real-world deployment. These findings underscore the potential of the DEA framework to serve as a proactive and efficient solution in mitigating the impact of ransomware attacks across diverse computing environments.