应对粮食安全、灾害风险、疾病暴发及社会经济脆弱性等关键全球挑战，亟需高保真度的地理空间建模。然而，构建预测性行星尺度模型仍受限于碎片化的数据生态体系，需人工检索数据、多模态数据整理与融合，以及迭代式模型选择。本文提出行星预测引擎（Planetary Prediction Engine, PPE），一种可直接响应自然语言查询、执行端到端工作流的自主人工智能系统。PPE 动态合成多模态数据集，从开放网络与地球观测平台（如 Data Commons、Google Earth Engine）中检索时空相关的协变量，并将其与地理空间基础模型嵌入（PDFM、AlphaEarth）相融合；同时，在任务定制的模型架构族中自动搜索最优模型，并内置防止过拟合机制。在涵盖不同任务、地理区域与科学领域的广泛评估中，PPE 始终优于当前最优方法或人工调优的专家基线模型。在美国空间回归任务中，PPE 在21项美国疾控中心（CDC）健康指标上的平均 $R^2$ 达76.8%（基线为60.0%），在联邦应急管理局（FEMA）国家风险指数上达64.9%（基线为60.0%），在社会脆弱性指数（Social Vulnerability Index）上达66.2%（基线为58.6%）。在数据稀缺场景下的空间降尺度任务中，PPE 通过整合本地化代理变量，将尼日利亚粮食安全指标的基线准确率提升一倍（$R^2$ 达66.1%，基线为31.5%）。在2026年刚果民主共和国布恩迪布戈埃博拉疫情的流行病学即时预测任务中，PPE 实现 Recall@10 为83.3%（在五次周度预测中识别出18个新入侵卫生区中的15个），较当前公开最优建模方法提升10.3个百分点（约73%）。通过将自主多模态行星数据发现与定向模型优化相结合，PPE 降低了……

Addressing critical global challenges, from food security and disaster risk to disease outbreaks and socio-economic vulnerability, demands high-fidelity geospatial modeling. However, building predictive planetary models remains bottlenecked by a fragmented data ecosystem, requiring manual data retrieval, multimodal data curation and fusion along with iterative model selection. We present the Planetary Prediction Engine (PPE), an autonomous AI system that executes this end-to-end workflow directly from natural-language queries. PPE synthesizes multimodal datasets on the fly, retrieving spatiotemporally relevant covariates across open-web and Earth observation platforms (Data Commons, Google Earth Engine) and fusing them with geospatial foundation model embeddings (PDFM, AlphaEarth). Simultaneously, it searches over task-tailored model architecture families with automated overfitting guards. Across diverse tasks, geographies, and scientific domains, PPE consistently outperforms state-of-the-art or manually tuned expert baselines. For US spatial regression, PPE improves mean $R^2$ across 21 CDC health indicators (76.8% vs. 60.0%), FEMA national risk indices (64.9% vs. 60.0%), and the Social Vulnerability Index (66.2% vs. 58.6%). For spatial downscaling in data-scarce settings, PPE integrates localized proxies to double baseline accuracy in Nigerian food security indicators ($R^2$ of 66.1% vs. 31.5%). For epidemiological nowcasting of the 2026 DRC Bundibugyo Ebola outbreak, PPE achieves a Recall@10 of 83.3% (identifying 15 of 18 newly invaded health zones across five weekly forecasts), a +10.3 percentage-point improvement over the public state-of-the-art modeling (~73%). By combining autonomous multimodal planetary data discovery with targeted model optimization, PPE lowers the technical barrier to planetary-scale analytics, enabling rapid, customized, expert-level deployment.