This chapter presents a comprehensive overview of thermo-kinetic modeling techniques for rigid polyurethane foams (RPUFs), emphasizing methods for accurately predicting foam properties essential to structural and thermal applications. Beginning with theoretical foundations, we outline the key exothermic reactions and complex reaction kinetics that influence foam formation, cellular structure, and insulation properties. The chapter then examines traditional methods, such as empirical models and numerical techniques like Finite Element Analysis (FEA) and Finite Volume Method (FVM), which provide essential, albeit limited, insights into RPUF behaviors. Modern techniques leverage data-driven, machine learning, and iterative modeling methods, greatly enhancing predictive accuracy. These approaches draw on extensive material databases and real-time data feedback, enabling more precise optimization of foam properties for diverse applications. The integration of these advanced methods has transformed RPUF research, reducing reliance on costly physical testing and enhancing model adaptability. The chapter concludes by contrasting traditional and modern methods, highlighting the long-term economic advantages of computational modeling. By offering a clear framework for selecting modeling approaches based on desired accuracy and resource availability, this chapter underscores the critical role of advanced modeling in the efficient, sustainable development of high-performance RPUFs.