Regenerative Medicine is advancing through the integration of computational methods, offering unprecedented insights and accelerating progress in biofabrication. These methods encompass sophisticated in silico methods, ranging from atomistic to continuum scales, to elucidate the complex dynamics of regenerative processes. Computational models play a pivotal role across various stages of biofabrication, including product modeling, biomaterial qualification, fabrication, and maturation. By leveraging computational algorithms and predictive modeling, researchers can meticulously characterize structural and functional attributes, assess candidate materials, optimize manufacturing processes, and guide tissue development in vitro. Moreover, artificial intelligence (AI) stand at the brink of transforming in silico modeling, enhancing predictive capabilities and enabling data-driven insights into complex biological phenomena. The integration of computational simulations with experimental frameworks holds promise for personalized regenerative therapies tailoring treatments to suit the specific requirements of each patient, heralding the dawn of a novel epoch in precision medicine in regenerative medicine research and clinical applications. This chapter elucidates the significance of employing in silico methodologies across distinct phases of tissue engineering, delineating their application at varying scales. Additionally, it discusses the integration of AI and machine learning within diverse realms of regenerative medicine, emphasizing existing challenges and prospective developments.