Ransomware attacks are now increasingly sophisticated, targeting critical systems and causing significant financial and operational damage to organizations worldwide. A novel digital forensic framework, designed for high-performance environments, offers real-time detection of ransomware anomalies within vast file system log data. The integration of machine learning techniques, such as isolation forests and support vector machines, with parallel processing across distributed systems enables the framework to detect previously unseen ransomware behaviors with remarkable accuracy. This scalable approach provides organizations with an efficient solution for identifying ransomware activity before significant damage occurs, even in complex computing environments. The framework's ability to process billions of log entries in real-time demonstrates its potential to handle the ever-growing volumes of operational data, ensuring timely detection and response. Moreover, the low false positive rates, combined with its adaptability to evolving ransomware variants, make the system a robust tool for enhancing cybersecurity defense mechanisms. The research contributes to the advancement of digital forensic methodologies, offering a practical solution for the early detection of sophisticated ransomware attacks.