Non‐destructive insight into the internal structure of sawn timber is critical for the wood industry, where knowing the central cross‐section (“mid‐slice”) of a board can improve strength grading and material utilization. We propose an AI‐driven pipeline that, given only photographs of the four external faces of a board, predicts the appearance of its cleaved median surface. To train such a model without the cost and labor of extensive manual scanning and annotation, we first generate a large synthetic dataset of virtual boards: a procedural 3D generator creates realistic knot geometries, annual‐ring patterns, and fiber orientations inside logs, and a diffusion model translates their boundary ring maps into photorealistic wood surfaces. From each virtual log we extract 2D renderings of its four faces plus the exact mid‐slice. These synthetic quintuplets form paired training examples for a convolutional neural network conditioned on four input images. At inference time, the network consumes real photographs of the left, right, front, and back faces of a board and outputs a predicted image of its internal cross‐section. Early experiments demonstrate that models trained on purely synthetic data can generalize to real timber. This approach promises to equip sawmills with a low‐cost, non‐invasive tool for internal board characterization, boosting grading precision and reducing waste.