Background: The exponential growth of cyber threats, particularly ransomware and Advanced Persistent Threats (APTs), has rendered traditional, reactive Security Operations Centers (SOCs) insufficient. As attack vectors diversify across social media, industrial control systems (ICS), and cloud environments, the volume of security telemetry exceeds human cognitive capacity.

Methods: This study synthesizes recent advancements in Artificial Intelligence (AI), Federated Learning, and Green Infrastructure to propose a "Cognitive-Green SOC" framework. We analyze a corpus of distinct studies, evaluating architectures ranging from Transformer-based threat identification to blockchain-enabled federated forests. The methodology integrates an AI-optimized playbook for ransomware investigation with dynamic workload optimization strategies to reduce the carbon footprint of intensive cryptographic computations.

 Results: The analysis demonstrates that integrating End-to-End architectures like RANK and Transformer models significantly improves detection rates for persistent attacks and social media threats compared to traditional heuristics. Furthermore, the integration of Green Infrastructure principles optimizes chip design and network loads, mitigating the high energy costs associated with continuous ML training.

Conclusion: The transition to Next-Gen SOCs requires more than just algorithmic upgrades; it demands a holistic architectural shift. By embedding explainable anomaly detection and prioritizing sustainable computing, organizations can achieve robust security postures that are both operationally efficient and environmentally responsible.