Shape Memory Alloys (SMAs) exhibit functional responses through a diffusionless, solid–solid phase transformation between austenite and martensite. This transformation generates fine-scale austenite–martensite microstructures with multiple crystallographic variants, whose arrangement is governed by geometric compatibility and energy minimization. In polycrystalline SMAs, three distinct length scales coexist—the microscopic scale of martensitic patterns, the mesoscopic scale of individual grains, and the macroscopic scale of a representative polycrystal—making predictive modeling challenging. Micromechanical approaches based on relaxation and homogenization provide a rigorous framework for linking these scales. Building on this framework, a three-dimensional, micromechanically consistent energy model for polycrystalline SMAs incorporates both intra-grain compatibility and inter-grain constraints without prescribing specific microstructures. The model is parametrized by microscopic material properties (elastic moduli, lattice strains, latent heat) and the polycrystal texture, and its nonconvex structure allows capturing material instabilities without phenomenological parameters. Practical implementation has been limited by the high cost of nested, high-dimensional, nonconvex optimization problems, especially the repeated evaluation of a mixing energy for each grain. To overcome this, we develop systematic approximations using semidefinite programming (SDP) and quadratic programming (QP), preserving key theoretical properties of the original energy. Numerical assessment on multi-variant NiTi microstructures shows that the SDP-based approximation achieves excellent accuracy, while the QP-based approximation balances accuracy and computational efficiency. These strategies reduce computational time by several orders of magnitude, enabling rapid evaluation of the effective polycrystalline energy. We demonstrate the model’s capabilities through three-dimensional simulations of textured NiTi under uniaxial tension, showing that it captures complex behavior and reproduces key experimental features of phase transformation and strain localization. The developments presented in this work also pave the way for future machine-learning-based strategies to accelerate simulations of structures incorporating polycrystalline SMAs.