Health technology assessment has undergone a profound transformation over the past decade, driven by accelerating biomedical innovation, rising uncertainty in evidence generation, and the increasing recognition that traditional static models of assessment are no longer sufficient for dynamic health systems. The COVID-19 pandemic further exposed structural weaknesses in conventional health technology assessment processes, particularly their inability to keep pace with rapidly evolving clinical evidence and the urgent need for real time decision making. In response, new paradigms have emerged that integrate real world data, adaptive regulatory pathways, managed entry agreements, living systematic reviews, and life cycle based assessment frameworks. These innovations collectively form the basis of what is increasingly referred to as living health technology assessment. This article develops a comprehensive theoretical and methodological synthesis of living and adaptive health technology assessment by integrating evidence from health economics, regulatory science, systematic review methodology, and payer decision making. Using the foundational literature on real world evidence, managed entry agreements, uncertainty management, automation of systematic reviews, and life cycle health technology assessment, the study builds a unified framework that connects evidence generation, synthesis, modeling, and reimbursement across the entire technology life cycle. The analysis demonstrates that living health technology assessment is not merely a technical evolution but a paradigm shift in how value, uncertainty, and learning are conceptualized in healthcare decision making. Real world data are shown to play a central role in enabling continuous updating of cost effectiveness, safety, and effectiveness estimates, while living systematic reviews and automation tools provide the epistemic infrastructure for maintaining an up to date evidence base. At the same time, managed entry agreements and coverage with evidence development are reframed as governance instruments that institutionalize learning under uncertainty. The results show that when these elements are integrated coherently, health systems can make earlier access decisions without sacrificing scientific rigor or fiscal sustainability. However, significant methodological, organizational, and ethical challenges remain, including data quality, bias, transparency, equity, and the alignment of incentives across stakeholders. By offering a deeply elaborated conceptual and operational model of living health technology assessment, this article contributes to the theoretical maturation of the field and provides a foundation for future empirical and policy research.