Knee joint mechanics, particularly the interactions between cartilage, menisci, and fluid pressure, play a critical role in maintaining joint function and mitigating conditions like osteoarthritis. Subject-specific finite element models have been widely applied to predict joint mechanics, but their applicability is limited due to high variability in knee joint geometry across populations. To address this limitation, this thesis aims to develop a comprehensive statistical shape model of the knee joint that integrates poromechanical principles and finite element analysis, providing a more generalized approach to studying knee joint mechanics. Using medical imaging data from 31 subjects, a three-dimensional statistical shape model was created to capture the population-wide variability in knee joint structures, including femoral and tibial cartilage, as well as the menisci. Advanced techniques such as principal component analysis were employed to identify the primary modes of geometric variation, with the first two principal components accounting for substantial changes in size and shape. Finite element simulations incorporated fibril-reinforced material models with fluid pressurization to analyze joint mechanics under various loading conditions, including long-term effects like creep and stress relaxation. Special emphasis was placed on simulating post-meniscectomy cases, where joint alignment and contact pressures were predicted for both intact and altered knee geometries. The results revealed significant morphological variations in the knee joint that influence mechanical behavior, particularly in terms of contact pressures and fluid pressurization. The synthetic models, created by altering the average shape along principal modes of variation, demonstrated how even small changes in knee geometry can significantly affect joint function. The combination of statistical shape modeling method with finite element analysis offered new insights into knee joint mechanics, especially in pathological cases such as meniscectomy, where maintaining joint stability and proper load distribution is critical. This research contributes to the field of computational biomechanics by establishing an efficient framework for integrating statistical shape modeling method with the finite element method. These models offer improved predictive power, reasonable computational cost for a large population, and broader applicability to clinical and surgical planning, making them potentially a valuable tool for advancing personalized medicine and enhancing implant designs.