Purpose: We evaluated differences in global and language-local networks in post-stroke aphasia associated with aging using diffusion tensor imaging (DTI)-based connectivity. Materials and Methods: Global and local metrics were extracted from deterministic tractography in fluent (n = 19; median age 60.0 years [interquartile range, IQR, 53.0– 68.0]) and non-fluent (n = 38; median age 61.0 years [IQR, 50.0–64.8]) aphasia patients.Brain-age estimation was performed using a pre-trained deep learning model from T13D data. Chronological age and brain-age estimation were used as control factors to find the distinct patterns of network characteristics between the groups, along with other factors such as sex, time from onset, total intracranial and gray matter volume. Results: Brain structure age estimation was 66.66 years (IQR, 62.74–70.13) for fluent and 72.14 years (IQR, 66.99–76.85) for non-fluent aphasia patients. There are no significant differences in chronological age (p = 0.859), but significant differences in brain-age estimation (p = 0.004 < 0.05). Our study revealed distinct network patterns between the groups. Regarding global metrics, higher clustering coefficient values were found in fluent (median 0.0113 [IQR, 0.0101–0.0123]) compared to non-fluent individuals (median 0.0094 [IQR, 0.0090–0.0106]) with family-wise error (FWE) p-value (pFWE) < 0.05.These differences were not retained when adjusting for brain-age (pFWE > 0.05). For local metrics, there was a lower clustering coefficient at right superior temporal gyrus (STG-R) and a higher degree at left STG (STG-L) in the fluent compared to the non-fluent group (pFWE < 0.05). These differences are more pronounced when incorporating age, brain-age, and multiple comparison corrections. Conclusion We observed the central hub role of STG-L, along with signs of neurological compensation reflected in the distribution of the STG-R in aphasia. The significant difference remained robust after correction for multiple comparisons and adjustment for age and brain-age, highlighting these nodes as a key discriminator between fluent and non-fluent aphasia.