Depression remains difficult to diagnose using objective biological markers, highlighting the need for biomarkers that are both replicable and generalizable. Electroencephalography (EEG) microstate analysis is proposed as a promising approach; however, findings remain inconsistent across studies. The present study evaluated the reliability and generalizability of EEG microstate temporal parameters using a multi-dataset framework. Resting-state EEG data from three independent datasets comprising healthy controls, subclinical depressive individuals, and depressed patients were analyzed using a standardized MICROSTATELAB pipeline. Microstate duration, occurrence, and coverage were extracted from a six-class solution. Group differences were assessed using Bayesian ANCOVA controlling for age and sex, and associations with depressive symptom severity (BDI) were examined using Bayesian linear regression. Replicability was further evaluated using a second recording within one dataset. Across datasets, most microstate parameters showed weak or anecdotal evidence for group differences. Moderate effects observed in one dataset, particularly in microstates A and G duration, did not replicate across datasets. Dimensional analyses revealed small and inconsistent associations with BDI scores. In pooled analyses, microstate C occurrence and coverage showed moderate to strong evidence; however, these effects were not observed within depression groups alone. Importantly, repeated recordings yielded different patterns of results, indicating limited within-sample replicability. These findings suggest that EEG microstate temporal parameters do not currently provide replicable or generalizable biomarkers for depression. While microstate dynamics may capture large-scale neural activity, their clinical utility remains limited. Future research should prioritize replication across datasets, standardized methodologies, and integration with complementary neural measures to establish reliable biomarkers.