This paper explores the connection between artificial intelligence (AI) and theoretical physics. Specifically, we focus on the Gravity-from-Entropy (GfE) approach, where gravity is derived from the geometric quantum relative entropy (GQRE) of two Lorentzian spacetimes. We show that the well-known Perona-Malik algorithm, used in image processing, is simply a gradient-flow GfE action. Specifically, this algorithm is the result of minimizing GQRE between the image's support and two Euclidean metrics induced by the image. The Perona-Malik algorithm is known to preserve sharp contours, which means that the GfE action does not lead to a uniform image, as would be expected when repeating gradient-flow dynamics. Rather, the result of GQRE minimization is compatible with the preservation of complex structure. These results provide geometric and information-theoretic foundations for the Perona-Malik algorithm and may contribute to building deeper connections between GfE, machine learning, and brain research.