Cyberattacks leveraging encryption-based extortion techniques have grown both in frequency and complexity, creating an urgent need for more advanced detection mechanisms capable of identifying malicious activity before significant harm occurs. The Dynamic Encryption Pattern Analysis (DEPA) system is introduced as a novel solution to address this challenge, focusing on the detection of ransomware through real-time monitoring of encryption behaviors. Unlike traditional methods that rely on static signatures or general anomaly detection, DEPA analyzes file modification patterns and entropy changes, allowing it to identify ransomware activity at an early stage. The system's adaptive detection algorithm further enhances its effectiveness, adjusting to evolving ransomware techniques through continuous feedback loops. Extensive experiments were conducted using a diverse dataset of ransomware families, demonstrating DEPA's high detection accuracy, low false positive rates, and efficient resource management across multiple operating systems and environments. In addition to its technical advantages, DEPA's modular architecture supports seamless integration into various cybersecurity infrastructures, making it suitable for large-scale deployments in both enterprise and cloud-based systems. This work highlights the potential of DEPA as a robust, scalable, and efficient solution for mitigating the rising threat of encryptionbased cyberattacks, offering a promising approach to enhancing automated ransomware detection capabilities.