The escalating complexity and adaptability of ransomware attacks have exposed critical gaps in traditional detection models, which struggle to effectively identify evolving threat behaviors without a significant margin of error. The Adaptive Pattern Scoring Mechanism (APSM) offers a novel approach to this challenge, leveraging a dynamic scoring framework that recalibrates based on real-time behavioral anomalies, enabling enhanced precision in distinguishing ransomware activities from benign events. APSM addresses inherent limitations in signaturebased and heuristic methods by integrating an adaptive scoring algorithm, which continuously adjusts detection thresholds and assigns differential weights across key behavioral dimensions, achieving a robust balance between detection accuracy and resource efficiency. Empirical evaluation reveals APSM's high accuracy and precision rates, with lower false-positive rates than conventional approaches, indicating its significant capability to detect ransomware with minimal operational disruption. The integration of dynamic scoring not only optimizes APSM's performance in real-time environments but also enables scalability across high-load conditions, marking a substantial advancement in automated cybersecurity infrastructures. Ultimately, APSM provides a sophisticated, resource-efficient solution that aligns closely with the requirements of modern ransomware detection, showing its value as a responsive tool in proactive threat management.