This chapter delves into the key factors in computational modeling of rigid polyurethane foams (RPUFs), focusing on four critical subtopics essential to advancing material performance and application. Prediction methods and modeling techniques are explored and introduced predictive modeling approaches in RPUF research. It highlights how methods like reaction kinetics, thermomechanical simulations, and multiscale modeling contribute to understanding and optimizing foam properties. Finite Element Modeling (FEM) is particularly emphasized for its robust simulation capabilities, helping researchers fine-tune characteristics such as compressive strength, insulation, and fire resistance. The composition and types of polyols, isocyanates, catalysts, and blowing agents are examined in depth. Computational models allow researchers to simulate these components to predict how variations will affect the foam’s structural integrity and performance, ultimately guiding formulation adjustments. Exploration of thermo-kinetic parameters, particularly reaction rates and heat transfer, impact RPUF’s mechanical strength and thermal stability. Modeling these aspects provides insight into controlling reaction kinetics for optimized foam stability, essential for applications requiring robust insulation. Heuristics means the use of simplified assumptions in computational modeling, which help balance accuracy with computational efficiency. This section reviews common heuristics, discusses their limitations, and advises on when detailed models may be preferable, guiding researchers in selecting the best modeling approach to achieve their specific RPUF development objectives.