Clustering is a fundamental task in unsupervised learning. Previous research has focused on learning-augmented $k$-means in Euclidean metrics, limiting its applicability to complex data representations. In this paper, we generalize learning-augmented $k$-clustering to operate on general metrics, enabling its application to graph-structured and non-Euclidean domains. Our framework also relaxes restrictive cluster size constraints, providing greater flexibility for datasets with imbalanced or unknown cluster distributions. Furthermore, we extend the hardness of query complexity to general metrics: under the Exponential Time Hypothesis (ETH), we show that any polynomial-time algorithm must perform approximately $\Omega(k / \alpha)$ queries to achieve a $(1 + \alpha)$-approximation. These contributions strengthen both the theoretical foundations and practical applicability of learning-augmented clustering, bridging gaps between traditional methods and real-world challenges.

Recent years have seen great progress in the approximability of fundamental clustering and facility location problems on high-dimensional Euclidean spaces, including $k$-Means and $k$-Median. While they admit strictly better approximation ratios than their general metric versions, their approximation ratios are still higher than the hardness ratios for general metrics, leaving the possibility that the ultimate optimal approximation ratios will be the same between Euclidean and general metrics. Moreover, such an improved algorithm for Euclidean spaces is not known for Uncapaciated Facility Location (UFL), another fundamental problem in the area. In this paper, we prove that for any $γ\geq 1.6774$ there exists $\varepsilon > 0$ such that Euclidean UFL admits a $(γ, 1 + 2e^{-γ} - \varepsilon)$-bifactor approximation algorithm, improving the result of Byrka and Aardal. Together with the $(γ, 1 + 2e^{-γ})$ NP-hardness in general metrics, it shows the first separation between general and Euclidean metrics for the aforementioned basic problems. We also present an $(α_{Li} - \varepsilon)$-(unifactor) approximation algorithm for UFL for some $\varepsilon > 0$ in Euclidean spaces, where $α_{Li} \approx 1.488$ is the best-known approximation ratio for UFL by Li.