The transition toward sustainable construction necessitates the upcycling of sawmill by-products, such as wood chips and sawdust, into high-performance biocomposites. These residues are advantageous because they retain wood’s native, high-strength microstructure while serving as a carbon sink [1,2]. However, the irregular geometries and heterogeneous nature of these constituents pose significant challenges for predicting macroscopic mechanical responses, particularly in nonlinear regimes [3]. The macroscopic response of such heterogeneous materials is dictated by the morphology and properties of their constituents. While classical mean-field homogenization schemes based on Eshelby-type solutions [4] are computationally efficient, they typically rely on idealized inclusion geometries and uniform strain assumptions that limit accuracy. This work proposes a hybrid framework integrating finite element-based Eshelby analysis with continuum micromechanics and machine learning to enable efficient nonlinear homogenization. The core innovation involves replacing single, average strain concentration tensors with discretized strain concentration fields extracted from FE simulations of non-ellipsoidal inclusions. By capturing these local heterogeneities, the framework enables the progressive plasticization of phases within continuum micromechanics. Specifically, a neural network is trained to predict clustered concentration tensors as a function of inclusion geometry and phase stiffnesses [5]. This allows the macroscopic nonlinear behavior to be computed by treating each cluster as an individual phase, ensuring results agree with full-field simulations without the need for explicit microstructure generation. As a critical extension, the framework is expanded to model curved fibers, allowing for a more realistic representation of the complex morphology and connectivity inherent in reassembled sawmill residues. This simulation-guided strategy provides a robust foundation for the design of next-generation 3D biocomposites, facilitating the transition from low-value wood waste to high-performance structural elements.