Abstract: Magnesium-based materials hold significant application prospects in lightweight structural components, biodegradable implants, solid-state hydrogen storage, and Mg-air batteries. Their performance is governed by the combined effects of composition, processing, microstructure, interfacial state, and service environment, while conventional trial-and-error experiments are inadequate to cover the resulting high-dimensional parameter space. In recent years, machine learning has been applied to these materials, extending from tensile property prediction to processing–microstructure control, corrosion and biodegradation evaluation, hydrogen-storage material screening, and anode and electrolyte design for Mg-air batteries. This review focuses on three interconnected aspects—data collection and feature engineering, model validation and interpretation, and candidate screening with experimental feedback—and critically evaluates the modeling methodologies and materials-related findings of representative studies. A cross-system comparison is presented among structural magnesium alloys, biomedical magnesium alloys, Mg-based hydrogen-storage materials, and Mg-air batteries, with emphasis on their differences in design variables, validation strategies, and evaluation metrics. Existing studies have demonstrated that compositional and conventional processing data are relatively abundant, whereas information on microstructural evolution, service environments, failed specimens, and cross-source validation remains insufficient. High-accuracy results obtained from random data splits cannot replace the validation through new alloys, new processing routes, or independent experiments. Future efforts should be directed toward establishing traceable and multi-source databases, incorporating constraints of phase stability, microstructural evolution, and electrochemistry into candidate screening, and enhancing the fabricability and reproducibility of designed materials through multi-objective optimization coupled with experimental feedback.