Effective human–machine interaction requires systems not only to perceive the current context but also to project future situations to proactively adapt to user needs. This paper introduces a situation projection framework based on rule mining, enabling machines to infer likely future states from past and present context data. The approach integrates knowledge discovery with cognitive interaction models, enhancing adaptability and responsiveness in complex environments. Experimental validation demonstrates that rule-based projection improves system performance in terms of situation awareness, offering a robust basis for next-generation interactive systems.