This study introduces a novel multimodal biometric fusion model that leverages artificial intelligence to enhance accuracy and generalization. The model integrates various fusion methods, including pixel-level, feature-level, and score-level fusion, through deep neural networks. Pixel-level fusion employs spatial, channel, and intensity strategies, while feature-level fusion uses modality-specific branches and jointly optimized representation layers. Score-level fusion employs techniques like Rank-1 and modality evaluation. The model's effectiveness is validated using a virtual homogeneous multimodal dataset constructed from simulated operational data. Experimental results show a significant improvement over single-modal algorithms, with a 2.2 percentage point increase in accuracy through multimodal feature fusion and a 3.5 percentage point increase through score fusion, achieving a retrieval accuracy of 99.6%. The study highlights the importance of multimodal biometric systems in enhancing security and recognition efficiency, addressing the limitations of single-modal systems.This study introduces a novel multimodal biometric fusion model that leverages artificial intelligence to enhance accuracy and generalization. The model integrates various fusion methods, including pixel-level, feature-level, and score-level fusion, through deep neural networks. Pixel-level fusion employs spatial, channel, and intensity strategies, while feature-level fusion uses modality-specific branches and jointly optimized representation layers. Score-level fusion employs techniques like Rank-1 and modality evaluation. The model's effectiveness is validated using a virtual homogeneous multimodal dataset constructed from simulated operational data. Experimental results show a significant improvement over single-modal algorithms, with a 2.2 percentage point increase in accuracy through multimodal feature fusion and a 3.5 percentage point increase through score fusion, achieving a retrieval accuracy of 99.6%. The study highlights the importance of multimodal biometric systems in enhancing security and recognition efficiency, addressing the limitations of single-modal systems.