Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. This study proposes a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine is a highly effective machine learning techniques used for categorization. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art techniques. The outcome of this study demonstrates that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM’s ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM demonstrates resilience in dealing with fluctuations in lighting conditions, stances, and facial expressions, rendering it highly suitable for gender recognition tasks in real-world video scenarios. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 9.10%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s.