Background: Candida spp. are opportunistic fungi whose pathogenic potential arises from interactions among multiple phenotypic traits that may not be captured by conventional single-factor analyses. This study aimed to identify multidimensional phenotypic virulence profiles among clinical Candida isolates using unsupervised machine learning. Methods: A total of 107 nasopharyngeal isolates recovered from COVID-19 convalescent patients in Almaty, Kazakhstan, during 2021–2022 were evaluated for biofilm formation, phospholipase, proteinase, hemolytic activities and susceptibility to fluconazole, voriconazole, itraconazole, and amphotericin B. Phenotypic data were analyzed using principal component analysis, Uniform Manifold Approximation and Projection, hierarchical clustering, permutational multivariate analysis of variance (PERMANOVA), Random Forest, and SHAP analysis. Results: Candida albicans predominated among the isolates (69.2%), and non-albicans Candida species accounted for 30.8%. Interspecies differences were observed in biofilm formation (p = 0.048) and itraconazole minimum inhibitory concentrations (p < 0.001). Three phenotypic clusters independent of species were identified (PERMANOVA: pseudo-F = 6.72, p = 0.0002). Across 20 repeated 5-fold cross-validation runs, the Random Forest model achieved an aggregated accuracy of 0.785, a mean balanced accuracy of 0.518 (95% CI: 0.461–0.577), and a macro-F1 score of 0.504 (95% CI: 0.444–0.559). SHAP identified itraconazole susceptibility, biofilm formation, and phospholipase activity as the strongest predictors. Conclusions: These findings demonstrate that integrating microbiological phenotyping with machine learning can reveal multidimensional virulence profiles beyond conventional species-based classification.