The accelerating sophistication, scale, and coordination of cyber threats have rendered traditional reactive security postures insufficient for modern digital ecosystems. As adversaries increasingly exploit the clear web, social media platforms, and the dark web to coordinate campaigns, trade exploits, and leak sensitive data, the strategic value of cyber threat intelligence has expanded from operational alerting toward anticipatory, intelligence-driven defense. This research article presents a comprehensive, theory-driven examination of contemporary cyber threat intelligence practices, with a particular focus on dark web monitoring, natural language processing, and the emerging role of large language models in transforming raw threat data into predictive security insight. Drawing strictly on established academic literature, industry research, and recent scholarly advancements, the article synthesizes crawler architectures, adversary behavior models, intelligence kill chains, and AI-driven analytics into a unified conceptual framework. Special attention is given to the epistemological challenges of trust, explainability, and bias in automated intelligence generation, as well as the operational implications of integrating threat intelligence into security information and event management systems. Through extensive theoretical elaboration, the article argues that the convergence of dark web intelligence harvesting, NLP-driven semantic enrichment, and LLM-powered reasoning marks a paradigm shift from reactive cybersecurity toward continuous, predictive threat hunting. The study concludes by articulating key limitations, ethical considerations, and future research directions necessary to ensure that advanced threat intelligence systems remain reliable, accountable, and strategically valuable in an increasingly adversarial digital landscape.