Abstract: Geo-hazards triggered by extreme weather events occur frequently in China and worldwide, posing severe risks to lives and property. Meteorological early warning for geo-hazards is a critical means of advancing disaster prevention “ahead of the event” and improving the effectiveness of mitigation efforts. In recent years, the increasing frequency of intense rainfall–induced geo-hazard clusters has highlighted the urgent need to enhance both model accuracy and system efficiency for precise early warning. This study provides a systematic review of progress in geo-hazard early warning models and operational systems, identifies their key challenges, and outlines future development directions. Geo-hazard meteorological early warning models fall into three major categories: statistical models, physical models, and machine learning models. Statistical models are widely used due to their simplicity and ease of implementation, but their accuracy is constrained by the quantity and quality of historical samples. Physical models can reveal failure mechanisms but face limitations in regional applications due to complex instability processes and parameter uncertainties. Machine learning models are rapidly advancing and offer strong potential for extracting multi-source data patterns and improving prediction performance; however, challenges remain in terms of limited samples and complex feature representation in real-world conditions. A global review of early warning practices in 28 major countries or regions shows that 82% of operational systems use statistical models, while 18% employ physical models. Research on machine learning models has surged in recent years, with some systems entering pilot testing and demonstrating promising prospects for broader application. Future efforts should focus on integrating multi-source geological–meteorological big data with advanced AI techniques to improve warning accuracy, strengthen response capability, and promote more precise and effective geo-hazard disaster prevention and mitigation.