Cybersecurity threats have escalated rapidly in recent years, with ransomware posing one of the most critical challenges to digital infrastructures globally. Traditional detection mechanisms, while effective against known threats, often lack the adaptability needed to address the fast-evolving tactics employed by ransomware attackers. The RansomGuard framework introduces a transformative, multi-layered approach to ransomware detection, integrating Dynamic Encryption Pattern Analysis (DEPA), Real-Time Behavior Profiling (RBP), and Heuristic Risk Scoring (HRS) to achieve unprecedented levels of accuracy and reliability. By employing adaptive analytical techniques, RansomGuard effectively distinguishes between benign and malicious activities, minimizing false positives and optimizing detection speed. Extensive evaluation across diverse ransomware families demonstrates RansomGuard's ability to achieve high detection rates while maintaining computational efficiency, ensuring its suitability for real-time deployment in dynamic and resourceconstrained environments. This study not only showcases a novel solution to the challenges associated with ransomware detection but also sets a new standard for cybersecurity frameworks aimed at proactive threat mitigation, positioning RansomGuard as a critical advancement in ransomware defense strategies.