Computational intelligence for 6G networks: A survey of machine learning and optimization for enabling technologies and architectures

6G wireless networks are envisioned to deliver pervasive, intelligent, and resilient connectivity across terrestrial, aerial, and space domains, supporting ultra-high data rates, sub-millisecond latency, massive device density, and tightly integrated communication, computation, and sensing. Realising this vision requires adaptive network intelligence capable of operating under uncertainty in highly dynamic environments, motivating the adoption of computational intelligence (CI), which brings together machine learning and optimisation for continual adaptation, autonomous decision-making, and robust control. Although a growing body of survey literature examines AI in 6G, most existing works focus on isolated technologies, application domains, or specific model classes, providing limited guidance on which CI methods are structurally suited to particular 6G tasks. This distinction matters because deep reinforcement learning, evolutionary algorithms, federated learning, graph neural networks, and fuzzy systems exhibit different inductive biases whose effectiveness depends on the underlying problem structure. This survey addresses this gap through a method-centric synthesis of CI across nine representative 6G enabling technologies: THz communications, reconfigurable intelligent surfaces, integrated sensing and communication, AI-native communications, extreme edge computing, zero-energy communications, space-air-ground integrated networks, semantic communications, and digital twins. For each technology, canonical tasks are identified, representative CI families are mapped to those tasks, and methodological trade-offs are analysed. The survey introduces the method–structure alignment principle as a unifying lens for CI method selection, identifies recurring conditions under which methods succeed or fail, and presents a consolidated research agenda for robust, scalable, cross-domain, and responsible CI in 6G.