We study multilingual speaker attribute prediction under linguistic variation, domain mismatch, and data imbalance across languages. We propose RLMIL-DAT, a multilingual extension of the reinforced multiple instance learning framework that combines reinforcement learning based instance selection with domain adversarial training to encourage language invariant utterance representations. We evaluate the approach on a five language Twitter corpus in a few shot setting and on a VoxCeleb2 derived corpus covering forty languages in a zero shot setting for gender and age prediction. Across a wide range of model configurations and multiple random seeds, RLMIL-DAT consistently improves Macro F1 compared to standard multiple instance learning and the original reinforced multiple instance learning framework. The largest gains are observed for gender prediction, while age prediction remains more challenging and shows smaller but positive improvements. Ablation experiments indicate that domain adversarial training is the primary contributor to the performance gains, enabling effective transfer from high resource English to lower resource languages by discouraging language specific cues in the shared encoder. In the zero shot setting on the smaller VoxCeleb2 subset, improvements are generally positive but less consistent, reflecting limited statistical power and the difficulty of generalizing to many unseen languages. Overall, the results demonstrate that combining instance selection with adversarial domain adaptation is an effective and robust strategy for cross lingual speaker attribute prediction.