As digital infrastructures continue to expand globally, organizations and individuals alike face an ever-growing threat from ransomware attacks, which disrupt critical systems and hold sensitive data hostage. The development of an adaptable and precise detection mechanism is crucial to counter the sophisticated and evolving tactics employed by ransomware actors. This research introduces the Dynamic Anomaly Matrix Embedding (DAME) framework, a highly novel approach designed to detect ransomware through a combination of matrix embedding techniques and dynamic behavioral profiling, achieving a substantial advancement over traditional detection models. DAME’s architecture allows for the real-time monitoring and analysis of system behaviors, effectively differentiating ransomware activity from legitimate processes with minimal false positives and negatives. Evaluation results demonstrate that DAME not only maintains a high detection accuracy and precision but also optimally balances processing speed and resource utilization, proving effective even in resource-constrained environments. Comparative analysis further reveals that DAME surpasses traditional detection models, such as Support Vector Machines and Random Forest, in both efficiency and adaptability to novel ransomware variants. The presented findings affirm DAME’s robustness as a cybersecurity solution and underscore its significant contributions to advancing autonomous ransomware detection methodologies.