Long-duration spaceflight exposes astronauts to microgravity-induced deconditioning and limited medical resources, demanding autonomous, predictive healthcare solutions. This paper presents a conceptual framework for an astronaut Human Digital Twin (HDT), an AI-driven virtual counterpart that integrates physiological, biochemical, environmental, and cognitive data to model astronaut adaptation in real time. As an initial implementation step, a microgravity stress test at a research facility equipped with a vertical treadmill apparatus will generate multimodal datasets on gait, cardiovascular, and musculoskeletal responses to simulated unloading. These data will train machine-learning models that classify astronauts by resilience or vulnerability to microgravity, forming the first HDT module focused on performance and deconditioning prediction. The framework enables early risk detection, personalized countermeasures, and reduced reliance on long-duration analogs, paving the way for onboard predictive medical systems and scalable applications for future deep space exploration missions.