The escalating frequency and sophistication of cyber threats necessitate the development of advanced detection mechanisms capable of operating autonomously and in real-time. The Real-Time Anomalous Entropy Analysis (RAEA) approach introduces a novel methodology that leverages entropy-based analysis to identify ransomware activities with high accuracy and minimal latency. By establishing baseline entropy profiles and employing adaptive anomaly detection algorithms, RAEA effectively distinguishes between benign and malicious processes, thereby reducing false positives and enhancing overall detection reliability. Empirical evaluations demonstrate RAEA's proficiency in detecting a diverse array of ransomware variants, including those employing sophisticated evasion techniques, while maintaining efficient resource utilization and scalability across various operational environments. The integration of RAEA into existing cybersecurity frameworks has the potential to substantially improve real-time threat detection and response capabilities, thereby mitigating the impact of ransomware attacks and contributing to the overall resilience of information systems.