Triply Periodic Minimal Surfaces (TPMS) are advanced porous geometries known for their high surface area, thermal efficiency, and tunable transport properties, making them ideal for thermal applications such as heat exchangers, energy storage, and catalytic systems. Accurate prediction of heat transfer in TPMS is essential for design optimization, as traditional methods like Computational Fluid Dynamics (CFD) are computationally expensive and impractical for iterative design. The Discrete Green’s Function (DGF) method, known for its computational efficiency through linear superposition, has been primarily applied to simpler geometries due to its dependence on analytical solutions or CFD-derived metrics. This work presents a novel framework that integrates DGF with Signed Distance Function (SDF)-based geometry representation, enabling mesh-free and rapid heat transfer evaluation in TPMS. By voxelizing the SDF, extracting geometric properties slice-wise, and constructing a convection-based DGF matrix via local temperature perturbations, the method allows efficient computation of heat flux and convective coefficients. This SDF-driven DGF approach retains physical accuracy while eliminating CFD dependence, offering a scalable and robust solution for rapid thermal analysis and optimization of complex porous media.