Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes, and accelerate cross-disciplinary innovation across the production pipeline. While broadly applicable to food systems, we focus on sustainable proteins--plant-based, fermentation-derived, and cultivated--as a high-impact testbed for AI-driven closed-loop design. We review the applications, opportunities, and challenges of AI for Food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing, and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery, and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.

翻译：全球食品系统必须提供营养、可持续的食品，同时大幅减少对环境的影响。然而，食品创新仍然缓慢、依赖经验且各自为政。人工智能提供了一条变革性路径，可将分子组成与功能性能联系起来，将化学结构与感官结果相关联，并加速整个生产流程中的跨学科创新。虽然人工智能广泛适用于食品系统，但我们聚焦于可持续蛋白质（植物基、发酵衍生及培养蛋白）作为高影响力的试验平台，以驱动闭环设计。本文综述了“人工智能+食品”这一新兴学科的应用、机遇与挑战，该学科整合了原料设计、配方开发、发酵与生产、质构分析、感官科学、制造及食谱生成。我们确定了四个优先方向：推进嵌入领域先验知识的科学机器学习、将食品视为可编程生物材料、构建用于自动发现的自驱动实验室，以及开发整合营养与可持续性的深度推理模型。将人工智能负责任地融入食品创新周期，可加速向可持续食品系统的转型，并建立一门以预测和设计为导向的食品科学，以服务于人类与地球健康。