Solubility prediction is a pivotal element in diverse fields such as drug discovery, material science, and environmental studies. This chapter delves into the computational tools and methodologies that facilitate accurate solubility predictions. It begins with classical methods like Raoult’s Law, Henry’s Law, and Hansen Solubility Parameters, providing foundational insights into solubility behavior. The discussion extends to advanced quantum mechanics-based approaches, including Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations, which offer atomistic-level precision in modeling solvation energies and interactions. Machine learning and data-driven approaches are emphasized for their ability to uncover complex patterns in chemical data using techniques such as neural networks, support vector machines, and random forests. These methods are bolstered by hybrid strategies that integrate quantum mechanics with machine learning, achieving improved accuracy and scalability. Real-world applications highlight the significance of computational solubility prediction in optimizing drug bioavailability, designing innovative materials, and assessing environmental impacts of pollutants. Furthermore, case studies underscore the practical implications of combining experimental data with computational insights to resolve solubility challenges efficiently. Emerging trends point towards the integration of multi-scale modeling, quantum-enhanced machine learning, and the use of open-access tools to advance predictive capabilities. The chapter concludes with a call for interdisciplinary collaboration and the development of standardized protocols to enhance the reliability and applicability of computational solubility prediction across scientific and industrial domains.