This thesis was conducted within Aerospace Propulsion Products, a company specialized in rocket engine igniters and Parachute Deployment Devices (PDDs) for space applications. The objective of this research was to develop a high-fidelity numerical model in Python capable of accurately predicting the performance of a PDD system. Given input parameters such as component geometry, propellant load, material properties, and environmental conditions, the model serves as a predictive tool to assist engineers in designing and optimizing PDDs for future planetary exploration missions. To achieve this, an extensive study of parachute ejection mechanisms was first conducted, analysing the physical principles governing gas generator combustion, gas flow dynamics, structural interactions, and aerodynamic forces. A numerical model was then formulated using fundamental principles of combustion, ballistics, physics, and thermodynamics, incorporating detailed representations of the gas generator, plenum, and parachute pack. Once developed, the model was calibrated using experimental data and systematically verified and validated against deployment tests to assess its accuracy in predicting key performance metrics such as pressure profiles, reaction forces, and ejection velocity. The validated model was then subjected to a sensitivity analysis to identify the most influential parameters affecting system performance. The results demonstrated that parameters such as the discharge coefficient, chamber volumes, plenum dimensions, and propellant loading significantly impact the pressure buildup, deployment timing, and ejection dynamics. Additionally, variations observed in experimental repeatability highlighted the inherent uncertainties in physical testing, further emphasizing the value of numerical modelling in PDD development. By providing a robust and accurate simulation framework, this research contributes to the advancement of next-generation PDD systems. The model developed in this study offers engineers a powerful tool for optimizing PDD designs, ensuring reliable deployment performance across various mission scenarios. Future work will focus on further refining the model by incorporating additional experimental data and extending its applicability to a wider range of deployment conditions.