Enhancing the efficacy of spinal cord injury (SCI) rehabilitation is crucial for a patient’s optimal recovery. While functional electrical stimulation (FES) cycling stands as a standard therapy, achieving notable improvements proves challenging due to the inherent complexities embedded in the dynamics of the movement. Indeed, overcoming the time-consuming nature of cycling becomes imperative, prompting the development of predictive models through optimal control simulation. The current challenge lies in the demand for a specific framework that considers the unique intricacies of SCI FES cycling. In response, our innovative approach introduces a novel framework and showcases its application in solving predictive models. Leveraging open-source tools, including OpenSim and Blender, we built the FES cycling model. Subsequently, we outlined predictive problems within OpenSim Moco. This advancement mitigates the time-consuming constraints of prior methods. This improved avenue for simulating FES cycling for SCI rehabilitation paves the way for practical and time-effective integration of Digital Twins in clinical applications.