This study aims to design MBPIFV, which is used to improve security, to address unimodal biometrics identification problem and to increase the accuracy of fingerprint and voice identification. All work is done in MATLAB environment except voice format converted by Wondershare audio Converter Ultimate and voice recorded by TECNOH8 Mobile. The system is designed by two main components such as fingerprint identification and voice identification component. Researcher design those components perform common activates such as preprocessing, feature extraction, training, and test. Finally fusion fingerprint with voice, and evaluate the performance of fingerprint, voice and Multimodal Biometric identification. During voice preprocessing perform Silence removal and pre-emphasis activity additional to feature extraction and HMM used for train and test voice. We use minutiae extraction and minutiae matching for fingerprint identification. To integrate fingerprint and voice first normalized by min-max approach then integrate using matching scores level fusion by sum rule. We use 20 fingerprints and 20 voices for training and testing, the fingerprint data obtained from FVC and the voice recorded from male and female on 8KZ frequency. The MBPIFV voice is evaluated using 20 voice data with 20 fingerprints, and the performance result shows fingerprint and voice give 85% and 65% accuracy respectively, and their combination gained 90 % accuracy result. Therefore, MBPIFV is very important to reduce fingerprint attacks such as; Spoofing, Exploit similarity, Zero-effort attempt, Replay attack, Denial of Service attack and Hill-climbing types of attacks. The main contribution of this thesis is to increase the accuracy of MBPIFV.