Chronic rejection remains a significant challenge in solid organ transplantation, contributing to graft dysfunction and eventual failure despite advances in immunosuppressive therapies. Computational modeling has emerged as a powerful tool for understanding chronic rejection mechanisms, enhancing diagnostic precision, and identifying novel therapeutic targets. This chapter explores various computational approaches, including artificial intelligence, machine learning, ordinary differential equations, partial differential equations, agent-based models, and gene network analysis applied to solid organ transplantation: kidney, liver, heart, and lung. While computational models offer numerous advantages, including cost-effectiveness and the ability to integrate multi-omics data, challenges remain in terms of data quality, standardization, and clinical validation. Bridging these gaps will require comprehensive longitudinal studies and the development of hybrid models that combine diverse computational techniques. Emerging technologies such as single-cell transcriptomics and spatial genomics hold promise for enhancing predictive accuracy and understanding cellular heterogeneity. As computational methods evolve, their integration with experimental research will be essential for developing precision medicine strategies to improve long-term graft survival.