Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities.

The advent of Artificial General Intelligence (AGI) promises to revolutionize affective computing. However, current research lacks a systematic framework that accounts for the inherently cross-space nature of human emotion, where physiological states (physical space), cultural norms (social space), cognitive processes (thinking space), and digital data (cyberspace) continuously interact and shape each other. To address this gap, this paper starts by repositioning affective computing within the theory of Cyber-Physical-Social-Thinking (CPST) space, conceptualizing it as a dynamic process of cross-space transformation rather than an isolated function. AGI capabilities of perception, learning, reasoning, memory, generalization, and generation serve as the computational engines that enable these cross-space transformations. We then systematically map AGI techniques (e.g., causal modeling, chain-of-thought, diffusion models) to specific affective computing tasks, showing how each technique supports their realization. By integrating CPST as a theoretical backbone and AGI as an enabling technology, this paper provides a systematic roadmap for next-generation affective computing. Potential applications and future directions are also discussed, highlighting how this framework advances human-level affective intelligence toward genuine empathy and contextual awareness.