Driven by the rapid advancement of artificial intelligence (AI), hyperfusion technology has become a critical enabler for the construction of a new-type power system under the “dual carbon” goal. AI-empowered hyperfusion demonstrates differentiated applicability and performance in various power industry scenarios, driven by the collaborative optimization of hyperfusion algorithms and AI, as well as the diversified demands from power system intelligent transformation. This article proposes three intelligent application paradigms of storage-computing fusion technology in power hyperfusion, aligned with AI development trends: machine learning-based memory computing, deep learning-integrated storage computing, and federated learning-supporting storage-computing architectures. It analyzes their core algorithmic optimization principles and advantages, matches them with AI-driven power applications like smart grids and new energy prediction, and outlines current high-performance intelligent algorithms with technical indicators. Challenges in the AI era are highlighted: insufficient interpretability of AI models for power reliability certification, inadequate integration of privacy computing and blockchain security technologies, and the lack of a complete power digital ecosystem for AI implementation. Future directions suggest leveraging cutting-edge AI big models to innovate basic algorithms and deploy intelligent applications, fostering an AI-native development model for power hyperfusion.