Modeling complex physical systems continues to be a major challenge in many fields of science and technology. In this talk we present a general framework, which advances such endeavor. Specifically, we describe the development of a machine-learning based model blending architecture for statistically combining multiple models for improving the accuracy of an application-specific forecast or prediction. Most importantly, we demonstrate that in addition to parameters to be predicted or forecasted, including additional state parameters which collectively define a situation as machine learning input provides enhanced accuracy for the blended result. ANOVA (analysis of variance) shows that the error of individual models can have a substantial dependence on the situation. The machine-learning architecture effectively reduces such situation dependence error and thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. The framework is first illustrated in the context of weather forecasting, which is arguably one of the hardest problems in physics, not only because of the complexity and scale of the problem but also given the additional complication that predictions/forecasts have to be made using limited observation at sparse locations. We will also demonstrate that the framework is applicable to other applications.