This paper provides a concise overview of the current state of research on digital twins and machine learning in smart farming, followed by three detailed case studies that illustrate their applications in smart farming for space and extreme environments. In the first case study, we evaluated the effectiveness of commercial machine-learning models using Azure Machine Learning for predicting plant growth. Lettuce growth was observed in two distinct environments, including one on a mountain with extreme weather conditions (acting as the physical twin-sender) and the other in a controlled growth chamber that replicated those conditions (serving as the physical twin-receiver). The mountain setting functioned as a “space surrogate,” while the growth chamber represented Earth-like conditions. Lettuce was cultivated using both soil-based pots and hydroponic solutions to assess different growing methods. In the second study, the growth of lettuce under different stresses in six climate zones on Earth was considered. Linear regression, ridge regression, lasso regression, polynomial regression, random forest regression, and boosted decision tree regression were used for predicting crop yield. Finally, this paper discusses the development of a digital twin using XMPro software for the growth of lettuce under abiotic stress conditions (temperature, light, water, oxygen in water) and different cultivation methods (simulated regolith as lunar soil, hydroponics).