Ransomware is one of the most critical cybersecurity threats, targeting systems globally and causing substantial financial and data losses. Developing an effective and efficient detection mechanism within operating systems is essential for mitigating these risks, which is where the novel integration of LSTM-based machine learning within the Linux kernel offers a significant advancement. The proposed approach involves the deployment of an LSTM model capable of analyzing system call sequences in realtime, identifying anomalous behavior indicative of ransomware activities with high accuracy. The research outlines the model's design, including data collection, preprocessing, and the intricate integration process into the Linux kernel to facilitate seamless communication and classification. Extensive experimental evaluation demonstrated that the LSTM model achieved superior detection performance when compared to traditional methods, maintaining a high level of accuracy and low false positive rates. The findings indicate that this method not only provides a proactive defense mechanism against emerging ransomware threats but also enhances the overall security framework of Linux systems through its adaptive learning capabilities and efficient resource utilization.