Ransomware attacks continue to escalate in frequency and sophistication, posing severe threats to global cybersecurity infrastructures. Recognizing the necessity for advanced detection methods, this research introduces a novel approach that leverages dynamic clustering and out-of-bound detection techniques to proactively identify ransomware activities in file system traffic. By integrating real-time data analysis and adaptive thresholding, the system ensures high sensitivity and specificity in detecting potential threats. The method's ability to adjust to evolving attack patterns and varied file system behaviors significantly enhances its practicality for robust cyber defense strategies. Results from extensive evaluations demonstrate that this approach not only accurately identifies ransomware signatures but also reduces false positives, making it a valuable tool for ensuring digital security.