Objective: We aimed to identify and characterize distinct subgroups of patients with psoriatic arthritis (PsA) based on their responses to the Assessment of SpondyloArthritis International Society Health Index (ASAS HI), using latent class analysis (LCA). Methods: We performed a latent class analysis on 17 dichotomous ASAS HI items in a cohort of patients with PsA (n: 90). A Gaussian mixture model was applied, and the optimal number of classes was selected based on the Akaike (AIC) and Bayesian Information Criteria (BIC). Class-specific response probabilities and class sizes were reported to describe the health impact patterns. Results: The best-fitting model identified six distinct latent classes. Class 2 (n = 32) represented patients with a low overall health impact across all ASAS HI domains. Class 5 (n = 14) showed very high impairment in both physical and emotional items. Intermediate profiles included Class 0 and Class 3, with predominant physical disability and partial emotional burden. Class 4 (n = 10) was characterized by broad impact, particularly in emotional domains, while Class 1 (n = 15) showed moderate physical limitation with preserved emotional function. The model confirmed substantial heterogeneity in perceived health status among PsA patients. Conclusion: Latent class analysis of the ASAS HI identified six clinically meaningful health impact profiles in PsA. These findings support the use of the ASAS HI as a multidimensional tool capable of capturing diverse patient experiences and may help inform individualized management strategies in PsA.