Magnesium (Mg) and its alloys hold great potential for biomedical applications, particularly as biodegradable implants, due to their biocompatibility, biodegradability, and favorable mechanical properties. However, achieving the optimal balance of mechanical strength, corrosion resistance, and biological performance remains challenging. Advanced computational tools play a pivotal role in addressing these challenges. CALculation of PHAse Diagrams (CALPHAD) modeling predicts phase stability and thermodynamic properties, while molecular dynamics (MD) and density functional theory (DFT) offer atomic-scale insights into diffusion and bonding mechanisms. Machine learning (ML) accelerates alloy design by analyzing large datasets to predict properties and optimize compositions efficiently. These tools are integrated within the framework of Integrated Computational Materials Engineering (ICME), enabling multiscale analyses that connect atomic interactions with macroscopic performance. Incorporating advanced simulations with experimental insights establishes a cohesive strategy for creating high-performance biodegradable Mg alloys. This chapter explores the use of advanced computational tools, including CALPHAD, MD, DFT, and ML for composition optimization and properties prediction of Mg alloys. The methodologies discussed herein highlight their potential to streamline alloy development processes, reduce time and costs, and enhance the precision and sustainability of material innovation.