Autoimmune diseases involve complex interactions between genetic, environmental, and immunological factors. Computational modeling has emerged as a powerful tool for understanding disease mechanisms, predicting progression, and identifying therapeutic targets. This chapter explores various computational approaches, including mechanistic modeling, machine learning, and network-based analyses, highlighting their applications in autoimmune disease research. Recent reviews by (Vivas et al. 2024) and (Danieli et al. 2024) emphasize the growing role of AI and machine learning in disease prediction and classification. By integrating diverse biological data such as genomics, proteomics, and clinical observations, computational models enhance diagnostic accuracy and treatment strategies. Despite advancements, challenges such as data heterogeneity, model interpretability, and clinical validation remain. Addressing these challenges will be crucial for advancing precision medicine and improving patient outcomes in autoimmune diseases.