This paper reviews the 2025 research work titled \"Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?\" by Konrad Mundinger, Max Zimmer, Aldo Kiem, Christoph Spiegel, and Sebastian Pokutta. The study presents a novel approach that uses neural networks to drive mathematical discovery. Focusing on the Hadwiger-Nelson problem—a long-standing open question in discrete geometry and combinatorics—the researchers reformulated a complex geometric coloring challenge into an optimization task that neural networks could solve. Through this method, they discovered two novel six-colorings of the Euclidean plane, achieving the first improvement in thirty years to a variant of the original problem. This paper provides a conceptual, equation?free explanation of the research, its methodology, key findings, and broader implications for the use of artificial intelligence in mathematical discovery.