The rapid evolution of ransomware poses an increasing threat to information security, with malicious actors deploying advanced techniques to evade traditional detection systems. Introducing a method for ransomware detection that leverages Gaussian algorithms for classifying low-level file system I/O Request Packet (IRP) patterns offers a novel and statistically grounded approach to identify deviations in system behavior. The methodology captures abnormal IRP sequences that signify ransomware activity, outperforming conventional signaturebased and heuristic detection techniques, especially when dealing with zero-day ransomware variants. A controlled experimental environment demonstrated the effectiveness of this approach, achieving high detection rates and minimizing false positives. Additionally, the model's adaptability to dynamic ransomware behavior positions it as a robust solution for environments where early intervention is critical. Results indicate that the Gaussian algorithm-based model provides a scalable and efficient method for real-time detection of ransomware, significantly enhancing the capabilities of modern cybersecurity frameworks.