Abstract: Forest management is the critical pathway for improving forest quality. Existing management models are constrained by the complexity of forest types and long growth cycles, resulting in coarse management practices, difficulties in implementing operational plans, low levels of intelligence, and high trial-and-error costs. These limitations hinder the realization of precise forest management objectives. To address these challenges, this paper first analyzes the transformation demands of forest management and decision-making from experience-based approaches to data- and intelligence-driven paradigms and elaborates the construction mechanism of forest digital twins, which transform forestlands into decision-making laboratories. On this basis, a digital-twin-driven framework for precise forest management and decision-making was proposed, encompassing key modules such as intelligent data perception and updating, stand structure analysis, forest growth modeling and intelligent simulation, intelligent formulation and optimization of management plans, precise operation execution, and intelligent evaluation and feedback of management performance. Using the Shanghai Forest of Saihanba Mechanized Forest Farm, Hebei Province of northern China as an empirical case, the paper systematically demonstrates the complete implementation pathway from digital twin construction, stand growth simulation, and stand structure analysis to forest management optimization. Finally, it discusses the opportunities and challenges facing this framework. The multiple strategic opportunities were created by “Digital China” strategy providing strategic leadership, biodiversity conservation and carbon neutrality goals driving initiatives, the demand for precise forest quality enhancement together with AI empowerment. However, critical technological bottlenecks remain to be addressed, including the integration and updating of multi-source heterogeneous data, efficient large-scale twin modeling, interpretable multi-objective optimization decision-making, and precise virtual-real interactive linkage. To this end, this paper outlines its future evolution toward deep integration with multimodal large models, embodied intelligence, generative artificial intelligence and the metaverse, aiming to promote precision, efficiency and intelligence in forest management and to empower the high-quality development of forestry and grassland sector.