The ever increasing sophistication of ransomware attacks demands the development of advanced detection methodologies capable of identifying and mitigating such threats in real-time. The Adaptive Sequence Mapping Analysis (ASMA) framework introduces a novel approach that leverages sequential pattern recognition and entropy-based metrics to detect anomalous behaviors indicative of ransomware activity. Through comprehensive empirical evaluation, ASMA has demonstrated high detection accuracy across a diverse array of ransomware families, effectively distinguishing malicious actions from benign processes with minimal false positives. The framework's adaptability to zero-day attacks and scalability in high-throughput environments underline its potential for integration into existing cybersecurity infrastructures, providing a proactive defense mechanism against the evolving landscape of ransomware threats. The modular architecture of ASMA facilitates seamless integration with current security systems, enabling organizations to enhance their protective measures without necessitating extensive overhauls of existing infrastructures. The computational efficiency of the framework ensures real-time detection capabilities, maintaining system performance even under substantial network loads. These findings position ASMA as a valuable tool in the ongoing efforts to safeguard digital assets from malicious encryption-based attacks.