The prevalence of sophisticated ransomware attacks targeting critical infrastructure, financial systems, and government networks demands detection methodologies capable of adapting to an evolving threat landscape. Autonomous Patternbased Signature Extraction (APSE), a novel ransomware detection framework, introduces an innovative approach to threat detection through autonomous signature generation and multi-level pattern recognition, advancing beyond conventional signature and heuristic-based techniques. APSE operates independently, leveraging unsupervised learning algorithms to analyze and classify ransomware behavior in real-time, generating dynamic and highly specific signatures without reliance on manual updates. Through extensive evaluation, APSE demonstrated high accuracy in detecting known and zero-day ransomware variants, outperforming traditional methods by effectively minimizing false positives and false negatives. Efficiency testing further revealed APSE's ability to operate within resource-constrained environments, optimizing memory and processing requirements without compromising detection rates. The system's modular design enhances scalability across diverse network architectures, making it suitable for integration within large-scale, automated cybersecurity infrastructures. By advancing the field of autonomous threat detection, APSE establishes a new standard in proactive ransomware mitigation, promising heightened resilience and security across critical domains vulnerable to ransomware attacks.