The escalation of ransomware attacks has become a critical threat to digital security, driven by increasingly adaptive and evasive malware that bypasses conventional detection methods. Addressing the limitations of signature-dependent and behavior-based approaches, this paper introduces a novel, autonomous framework known as Temporal Signature Decomposition Analysis (TSDA), designed to detect ransomware through the decomposition of unique temporal event patterns. TSDA operates independently of predefined threat signatures, leveraging timeseries analysis and statistical pattern recognition to identify ransomware behaviors based on time-dependent anomalies, thus achieving a high detection rate with minimal false positives. Through extensive experimental evaluation, TSDA demonstrates both high detection accuracy and low latency, enabling it to function effectively in environments requiring rapid and reliable ransomware identification. Moreover, the method's scalability and efficient resource utilization establish it as a viable solution for large-scale ransomware defense across diverse operational domains, reducing the need for human oversight while ensuring resilience against sophisticated attack tactics. The study's findings demonstrate TSDA's potential as a transformative tool in advancing ransomware detection, providing a sophisticated, adaptable, and robust addition to modern cybersecurity frameworks.