Standard eddy-viscosity models lack curvature and system rotation sensitized terms in their formulation. Hence they fail to capture the effects of curvature and system rotation on turbulence anisotropy. As part of this effort, an algebraic expression for a characteristic rotation term is developed and tuned with the help of rotating homogeneous shear flow. This formulation is primarily based upon the rotation and curvature sensitized eddy-viscosity coefficient developed by York et al. (2009). A new scalar transport equation loosely based on Durbin’s wall normal turbulent velocity scale (Durbin, 1991) is introduced to account for the modification in turbulence structure due to system rotation and curvature effects. The added transport equation also introduces history effects and stability in the solution with small increase in computational cost. The eddy-viscosity is redefined based on new turbulent velocity scale and hence the effects of rotation and streamline curvature are introduced into the mean momentum equation. A number of canonical test cases with significant curvature and rotation effects along with a cyclone flow, a representative of complex industrial flows, are considered for model validation. Hybrid modeling framework combines the strength of RANS in boundary layers and LES in separated shear layers to alleviate the weaknesses of RANS and limitations of LES model in some complex flows. A recently proposed hybrid RANS-LES modeling framework uses a weighing parameter that dynamically determines the RANS and LES regions based on solution statistics. The hybrid modeling methodology is implemented on a normal jet in crossflow, and a film cooling case for the purpose of model validation and evaluation. The final goal of the proposed effort is to combine advanced RANS modeling capability with LES using the new hybrid modeling framework. Specifically, the curvature and rotation sensitive RANS model developed here is coupled with commonly used LES models to produce a novel model for complex turbulent flows with the potential to improve accuracy of CFD predictions (versus existing RANS models) as well as significantly reduce the computational expense (versus existing LES models). Performance of the model form hence developed is evaluated on a cyclone flow case.