Abstract: The transition to carbon neutrality is a complex, multi-scale, cross-domain coupled system engineering endeavor, with breakthrough innovations in low-carbon energy materials at its core. Facing structural challenges such as the highly nonlinear nature of material design space, conflicting multi-objective goals, and lengthy R&D cycles, Artificial Intelligence (AI) is reshaping the research paradigm in materials science. From computational acceleration and knowledge mining to closed-loop decision-making, AI is progressively evolving from an auxiliary tool into the digital infrastructure underpinning innovation in low-carbon energy materials. This paper systematically analyzes AI's role in the carbon neutrality transition process, examining its application progress across three core energy systems: energy production, energy storage, and energy conversion. It identifies key technological pathways including data fusion, physical constraint modeling, and automated experimental integration. Future breakthroughs will center on explainable and trustworthy AI frameworks, multi-objective adaptive optimization, intelligent experimentation and manufacturing, alongside technological ethics and institutional governance. The deep integration of AI and materials science will propel low-carbon energy technologies from experience-driven approaches toward intelligence-driven decision-making, providing structural technological support for achieving carbon neutrality goals.