<p>Cognitive Digital Twins (CDTs) offer a transformative approach to road pavement management by integrating real-time data, advanced modeling, and artificial intelligence for monitoring, maintenance, and performance prediction. This review synthesizes insights from over 100 studies and provides a comprehensive examination of the technological foundations, applications, and ecosystem required for CDT deployment in pavement systems. Unlike existing Digital Twin (DT) reviews that focus primarily on general infrastructure or isolated technical components to date, this study situates CDTs within a broader system-of-systems context; linking pavement management to smart cities, Industry 4.0/5.0 paradigms, environmental sustainability, and data-governance challenges. In addition to summarizing state-of-the-art CDT architectures and Artificial Intelligence (AI)-driven analytics, the review evaluates how CDTs interact with wider urban infrastructure ecosystems, including Internet of Things (IoT) networks, Building Information Modeling (BIM) integration, real-time sensing, and multi-level decision-making frameworks. It further highlights cross-cutting issues seldom addressed in prior reviews, such as data security, cyber-physical vulnerabilities, privacy preservation, and the environmental implications of digitalization. Key barriers are examined alongside critical research gaps such as limited long-term validation, insufficient sustainability incorporation, and the lack of holistic deployment frameworks. Future directions emphasize the need for standardized CDT architectures, advanced computational models, secure data infrastructures, and sustainability-driven performance metrics. Overall, this review positions CDTs not merely as a technical innovation but as a pivotal enabler in the transition toward resilient, sustainable, and intelligently managed smart cities.</p>