The rapid evolution of smart cities has been driven by the convergence of digital technologies, intelligent infrastructure, and data-driven governance models. However, despite significant advancements in artificial intelligence, automation, and urban analytics, contemporary smart city ecosystems remain fragmented, operationally inefficient, and constrained by siloed decision-making processes. This research addresses these limitations by developing and theoretically validating an integrated framework that combines hyperautomation, generative artificial intelligence, process mining, edge intelligence, and smart infrastructure management to enable adaptive, resilient, and human-centric smart cities. Drawing strictly from the provided scholarly and industry references, the study synthesizes insights from hyperautomation literature, artificial intelligence adoption in urban contexts, smart city security and governance research, and edge–cloud architectural models for energy and infrastructure optimization. The methodology adopts a qualitative, theory-driven research design grounded in extensive conceptual analysis, cross-domain integration, and interpretive synthesis of prior empirical findings. Results indicate that hyperautomation, when augmented with generative artificial intelligence and process mining, enables continuous optimization of urban workflows, enhances transparency in governance, and supports real-time adaptive decision-making across energy, mobility, public services, and financial systems. Furthermore, the integration of edge intelligence and tiny machine learning architectures addresses latency, privacy, and scalability challenges inherent in large-scale urban environments. The discussion elaborates on the socio-technical implications of this integration, emphasizing trust, security, ethical governance, and citizen participation as critical success factors. Limitations related to data heterogeneity, institutional readiness, and regulatory fragmentation are critically examined, alongside future research directions focusing on autonomous governance models and participatory AI systems. The study concludes that a unified hyperautomation-driven smart city framework represents a transformative paradigm capable of aligning technological innovation with sustainable urban development and societal well-being.