Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model was evaluated on the SDUMLA-HMT dataset, which contains a variety of biometric features from various individuals. The system outperformed existing techniques in the literature with an excellent recognition accuracy of 99.4%. Although this result is encouraging, further research on larger and more varied datasets is necessary to confirm its applicability across many circumstances. This study highlights how multimodal biometrics strengthened by metaheuristic algorithms can considerably increase biometric security against spoofing assaults, thereby opening a promising new direction for future development in the field.