This thesis delivers a thorough analysis of lithium-ion battery monitoring technologiesand the discovery of new functional materials. It starts with an exploration using Transient Grating Spectroscopy (TGS) to implement a non-destructive technique for assessing the state of lithiation in battery cells. This approach yields precise observations of the internal behaviors of battery cells under diverse conditions, particularly with a focus on lithium electrodeposition. Through the study of surface acoustic waves (SAW), which are highly sensitive to microstructural changes on electrode surfaces during nucleation and growth, the potential of TGS for in-situ monitoring of electrochemical interfaces and identifying defects in battery electrodes is emphasized. The research progresses to examine the freeze-thaw dynamics of lithium-ion battery graphite electrodes at extreme temperatures to evaluate battery durability and performance under conditions similar to those on lunar missions. Analyses using the time of flight and damping properties of Bulk Acoustic Waves (BAW), coupled with electrochemical assessments, provide insights into the mechanical responses of batteries to significant temperature variations, essential for space missions and other harsh environments. Furthermore, the thesis investigates the role of AI in materials science, particularly how large language models can autonomously generate a database of magnetocaloric effect (MCE) materials from published sources. This AI-enhanced method identifies promising MCE materials across various temperatures, confirmed through density functional theory simulations and automated structural determinations. The integration of these discoveries into a self-updating database emphasizes the transformative role of AI in advancing material discovery, and in the future, it could prove invaluable for developing battery components such as cathodes, anodes, and electrolytes