Emerging threats in cybersecurity require innovative approaches to counter increasingly complex attacks targeting critical systems. A novel detection framework has been proposed, leveraging the concept of Proactive Cryptographic Residue Mapping to address challenges posed by obfuscated and polymorphic ransomware variants. Through systematic analysis of bytecode sequences and cryptographic residues, the methodology integrates entropy-driven insights with advanced feature extraction to distinguish malicious encryption behaviors. Automated classification pipelines enhance accuracy while minimizing computational overhead, ensuring scalability across diverse deployment environments. Experimental results demonstrate robust detection performance, achieving high accuracy and low falsepositive rates across multiple ransomware families. The use of sequential analysis further mitigates the impact of variantspecific obfuscation, enabling reliable identification of malicious patterns. Processing efficiency evaluations highlight the framework's suitability for resource-constrained applications, offering practical implications for real-time threat mitigation. Comparative analyses against traditional methods underline significant improvements in both accuracy and robustness. Scalability assessments indicate consistent performance across varying dataset sizes and computational configurations. Entropy distribution studies provide deeper understanding of cryptographic behaviors, reinforcing the methodological foundations. Error rates under simulated noise levels reveal opportunities for further refinement, especially in challenging environments. The proposed framework presents a significant contribution to automated cybersecurity solutions, balancing analytical sophistication with operational feasibility.