The ongoing evolution of cyber threats has demanded increasingly sophisticated detection methods to counteract ransomware, which continues to impose severe risks on global systems. The Behavioral Entropy Detection Protocol (BEDP) introduces a real-time ransomware detection framework that leverages entropy-based metrics and behavioral analysis to identify ransomware activities before significant harm occurs. BEDP's design incorporates a dual-layered architecture, combining entropy thresholding with machine learning classifications to detect ransomware-linked file manipulations and encryption patterns, enabling it to effectively handle both known and unknown ransomware variants without reliance on pre-existing signatures. Extensive testing revealed BEDP's high accuracy and low false positive rate across various datasets, achieving significant reductions in computational resource usage while ensuring scalable performance in high-frequency file environments. The protocol's hierarchical resource allocation further optimizes entropy calculations, demonstrating low latency and reliability in real-time applications. Findings indicate BEDP's robust adaptability within enterprise-level cybersecurity infrastructures, where rapid detection of ransomware is essential, showing its suitability for deployment in both decentralized and large-scale networks. BEDP's innovative use of entropy metrics presents an adaptable, efficient approach to ransomware defense, establishing a foundation for the enhancement of proactive security measures.