Effective detection of sophisticated cyber threats requires innovative frameworks capable of analyzing the complex and evolving behaviors of malicious software. The Temporal Behavior Chain Analysis (TBCA) framework introduces a novel methodology for identifying ransomware activity through the examination of temporally dependent system interactions. By constructing directed acyclic graphs to represent behavioral sequences, the framework captures subtle anomalies that traditional detection methods fail to identify. Machine learning techniques, including Hidden Markov Models and neural networks, enhance the framework's ability to discern malicious patterns from benign behaviors with high accuracy and efficiency. Evaluations demonstrated superior detection rates across multiple ransomware families, including LockBit, BlackCat, and Hive, even when advanced obfuscation techniques were employed. The modular design of TBCA ensures seamless integration into existing infrastructures while maintaining scalability for deployment in diverse operational environments. Resource efficiency was validated through extensive scalability testing, which confirmed the framework's capability to process high volumes of data with minimal computational overhead. Comparative analyses revealed significant improvements over traditional detection approaches, particularly in handling emerging and previously unseen ransomware variants. Temporal modeling of sequential dependencies enables early detection, reducing potential system damage and facilitating real-time mitigation strategies. The integration of adaptive decision-making mechanisms minimizes false positives, enhancing reliability in high-stakes environments. Cross-platform evaluations further highlighted the robustness of the framework, demonstrating its effectiveness on Windows, Linux, and macOS systems. The TBCA framework establishes a comprehensive foundation for advancing proactive cybersecurity defenses against increasingly complex and dynamic threats.