Nowadays practical solutions of engineering problems involve model-integrated computing. Model based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Due to their flexibility, robustness, and easy interpretability, the application of soft computing, in particular fuzzy and neural network based models, may have an exceptional role at many fields, especially in cases where the problem to be solved is highly nonlinear or when only partial, uncertain and/or inaccurate data is available. Nevertheless, ever so advantageous their usage can be, it can still be limited by their high computational complexity. Although, a possible solution can be, if we combine soft computing and anytime techniques, because the anytime mode of operation is able to adaptively cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this paper, a soft computing based anytime modeling methodology is presented for handling resource insufficiency and the applicability of the generated models is analyzed in dynamically changing, complex, time-critical systems.