The increasing complexity and prevalence of modern cyber threats have transformed the protection of critical infrastructures and sensitive data into a vital challenge in the digital age. In this context, the convergence of emerging technologies has been proposed as an innovative approach to redefine cybersecurity paradigms. This research, focusing on the combined potential of these technologies, explores the feasibility of transforming cognitive computing from a theoretical concept into an operational tool for countering advanced threats. Utilizing the meta-synthesis method and following the seven-step framework of Sandelowski and Barroso, this study systematically analyzes the domains of cloud computing, quantum computing, and cognitive computing. Accordingly, a three-layer conceptual model has been developed: the infrastructure layer integrating cloud resources and quantum computing, the cognitive layer encompassing self-learning algorithms and cognitive analysis, and the application layer optimizing security decision-making. The convergence of these technologies results in reduced anomaly detection time, improved attack prediction accuracy, and optimized computational resource consumption. Furthermore, the integration of quantum processing in the infrastructure layer enables the resolution of complex nonlinear security problems at scale. The findings confirm that the proposed ecosystem not only enhances cybersecurity situational awareness but also, through reinforcement learning and intelligent resource distribution, provides the capability to respond to unknown threats. This research marks a significant step towards transforming cognitive computing into a scalable autonomous system that can serve as a preventive solution in national cybersecurity architecture.