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Biometrics is a new and promising technique for identifying and authenticating humans. The traditional method of authenticating a person and determining their identity is resolved. Feature-level fusion is used to improve performance even further. Gabor filter methods were employed for feature extraction. The major goal of this study is to create and present a hybrid system based on multi-biometric fingerprint, face, and iris identification that combines two successful machine learning models: Support Vector Machine (SVM) and Random Forest (RF) classifiers. To assess the effectiveness of the suggested model, optimization techniques such as Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) were investigated. The experimental results take into account a variety of parameters such as Equal Error Rate (EER), False Acceptance Rate (FAR), False Rejection Ratio (FRR), and Accuracy, demonstrating that the proposed model outperforms other optimization models in terms of proving whether the claimed identity is genuine or imposter.