Spoken language identification (SLID) has gained prominence in modern applications with the advancement of artificial intelligence and machine learning. Its importance is particularly evident in multilingual societies, where identifying the language of spoken content enables efficient voice-based interactions. However, accurate SLID remains challenging due to linguistic similarities among related languages and the variability introduced by speakers, acoustic environments, content, and demographic factors such as age and gender. To address these challenges, this study proposes the Robust Optimization (RO-SLID) model, which integrates BiLSTM with Dung Beetle Optimization (DBO) for robust feature selection and classification. The proposed method was evaluated on the IIIT Spoken Language dataset and achieved an accuracy of 95.55%, precision of 84.46%, and recall of 84.41%, outperforming state-of-the-art models such as RF, LDA, DL-SLID, GA-SLID, and VGG-16. Furthermore, comparisons with bio-inspired computing models that include PSO and ACO highlight the superior performance of RO-SLID, with PSO achieving 82.13%, ACO achieving 89. 45% versus the maximum accuracy of the proposed model of 95. 55%. These results demonstrate the effectiveness of RO-SLID for multilingual voice processing and its potential for deployment in real-world applications.