Multiple Sclerosis (MS) is a chronic, neuroinflammatory disease of the central nervous system. Studying its progression presents significant hurdles. Research on human patients requires lengthy follow-up periods and necessitates stringent approval from ethics committees. Meanwhile, induced MS in animal (murine) models provides only an approximation of the human condition; their results may not translate to humans and are governed by strict ethical precautions. These limitations highlight the critical need for alternative research methodologies to accelerate understanding and therapeutic development. In this context, computational (in silico) modeling offers a powerful parallel approach. This technique uses mathematical frameworks to simulate highly complex systems, allowing months of disease progression to be analyzed in minutes, free from the ethical constraints of biological studies. However, a multitude of computational models exist, each based on specific hypotheses about the disease’s mechanisms, creating a fragmented landscape. This paper addresses this gap through a systematic literature mapping. From an initial pool of 199 studies, 49 relevant articles were selected. The primary contribution is identifying mathematical models that can be integrated, aiming to build a more comprehensive simulation framework that covers a broader spectrum of the disease’s developmental stages, thereby fostering a more holistic understanding of MS.