The growing sophistication of cyber threats has intensified the challenges faced by security systems, particularly in detecting malicious activities that exploit advanced encryption and behavioral evasion tactics. Dynamic Crypto-Behavioral Profiling (DCBP) offers a novel framework that combines realtime cryptographic monitoring with adaptive machine learning to address the limitations of traditional and static approaches. Through the continuous analysis of execution patterns, cryptographic operations, and network behaviors, the proposed system demonstrates exceptional accuracy in identifying both known and novel ransomware variants. The system's ability to adapt dynamically to evolving threats while maintaining high performance across diverse environments underscores its potential to redefine automated cybersecurity defenses. Comprehensive experimental evaluations illustrate its scalability, efficiency, and robustness, establishing a transformative direction in the field of malware detection and prevention.