Ion hydration is a central topic of discussion in the scientific community, given the important role that solvated ions play in fields as biochemistry, electrochemistry, and environmental chemistry. Despite the large amount of experimental and theoretical studies that involve ions in aqueous solutions, a unified molecular description is still missing: existing models fail to accurately capture the intricate ion-water interactions, leading to inconsistencies between experimental and theoretical results. The surface propensity of halide ions at the air/water interface has been debated for over a century, and the recent introduction of new architectures for the description of molecular interactions allows for the development of accurate and efficient models to describe their behavior.Advanced molecular modelling frameworks based on deep neural networks (DNNs) and many-body expansion (MBE) of the energy are explored and their limitations analysed. Data-driven MBE-based models as MB-nrg are able to provide chemical accuracy at great computational efficiency when compared to classical force fields (FFs), DFT-based ab initio molecular dynamics (AIMD), and modern DNN potentials. After developing an efficient active learning (AL) framework for the generation of comprehensive training sets, we proceed with the development of MB-nrg models for the description of the chloride, bromide, and iodide ions; these provide great accuracy in the description of both gas-phase clusters and bulk systems, by closely reproducing coupled cluster (CC) interaction energies and the experimental x-ray absorption spectra. Lastly, we address the errors associated with the use of semi-local exchange-correlation functionals in modeling hydrated ions within density functional theory (DFT): the recent introduction of density-corrected (DC) SCAN functionals provides a solution to the overdelocalization issue typically encountered in semi-local density functional approximations (DFAs), leading to considerable improvements in the energetics and structural features of hydrated ions. This dissertation presents a significant advancement in the understanding of ion hydration, showcasing novel methods for more precise theoretical predictions, and providing a solid foundation for future research in this challenging field.