The Predictive Code Anomaly Signatures (PCAS) framework represents a significant advancement in the proactive detection of ransomware threats. By integrating static code analysis with sophisticated anomaly detection algorithms, PCAS effectively identifies malicious code patterns indicative of ransomware activity. This innovative approach models code features as multi-dimensional vectors, capturing both syntactic and semantic characteristics to distinguish between benign and malicious code segments. The framework's scalability and modular adaptability facilitate seamless integration into diverse computing environments, enhancing its practical applicability. Empirical evaluations demonstrate that PCAS achieves a high true positive rate and a low false positive rate, indicating its robustness in accurately detecting ransomware threats. Furthermore, the ability of PCAS to bridge static and dynamic analysis paradigms addresses limitations inherent in traditional detection methods, offering a comprehensive solution for preemptive ransomware identification. The implementation of PCAS in cybersecurity infrastructures has the potential to significantly enhance defenses against evolving ransomware threats, providing real-time detection capabilities and enabling prompt responses to mitigate the impact of such incidents. Overall, the PCAS framework represents a substantial contribution to the field of cybersecurity, offering a novel and effective approach to ransomware detection.