<p>High Temperature Aquifer Thermal Energy Storage (HT-ATES) is a promising sustainable energy storage solution, capitalizing on the stable and continuous nature of geothermal energy. Research has advanced significantly since the pioneering field tests in the 1970s. Existing field studies demonstrated high storage capacity and heat recovery efficiency (&gt; 60%). Recent well-defined laboratory-scale experiments enhanced our understanding of thermal behavior and storage efficiency utilizing advanced techniques. Numerical simulations are now crucial, evolving from single-physics models to comprehensive multi-physics coupling frameworks (Thermal-Hydraulic-Mechanical (THM), Thermal-Hydraulic-Chemical (THC) or even Thermal-Hydraulic-Mechanical-Chemical (THMC)), which are essential for simulating heat transfer, fluid flow, rock deformation, and chemical reactions. High-performance computing platforms boosted the efficiency and accuracy of simulations, especially for the characterization of dynamic reservoir permeability and porosity changes. Furthermore, integrating machine learning with these multi-physics models contributed to effectively improving predictive accuracy and optimizing HT-ATES system design. Despite these advances, challenges remain, particularly in accurately capturing complex fractured reservoirs, mitigating thermal losses, ensuring long-term stability, and reducing high operational costs. Moving forward, overcoming these challenges through continued advancements in experimentation, in sophisticated multi-physics modeling, and in technological innovation is vital for establishing HT-ATES as a key component of global sustainable energy storage solutions. This review uniquely synthesizes advances in multi-physics coupling models and machine learning for HT-ATES, addressing gaps in previous reviews that overlooked system optimization and fractured reservoir studies.</p>