In this study, TurkSV-Bench is introduced as a novel benchmark dataset to evaluate the zero-shot cross-lingual performance and demographic biases of Turkish speaker verification (SV) systems. Created via an automated pipeline from YouTube data, this dataset comprises 1,844 audio recordings from 461 speakers representing diverse age groups, genders, and regional dialects. Evaluation of existing models identifies ResNet34-LM as the most successful baseline architecture. Subgroup analyses demonstrate the models’ generalization capabilities across different gender and age demographics, while revealing distinct performance variations among regional dialects. Comprehensive evaluations of various preprocessing techniques highlight the baseline architecture’s structural robustness, identifying specific configurations that maintain or refine verification performance without requiring aggressive signal filtering.