When does K-Means become a bad choice in modern ML systems?

K-Means becomes unsuitable when data is non-Euclidean, high-noise, or has complex manifold structure.

Modern datasets often involve embeddings, graphs, or sequential patterns where Euclidean assumptions break. In such cases, clustering requires density-based, graph-based, or probabilistic models instead of centroid-based partitioning.