Multimodal biometric authentication is becoming increasingly important for protecting sensitive information. This paper presents a comprehensive study of multimodal biometric systems that combine multiple biometric traits to enhance authentication accuracy and security. A comparison of several biometric approaches reveals that feature-level fusion combined with deep learning methods like CNNs is more significant. The paper provides an in-depth review of current advancements with datasets used (i.e., FV-USM and SDUMLA-HMT) and fusion strategies by contributing valuable insights for future improvements in biometric systems. The study addresses the challenges of multi-model systems including high error rates, noise interference, and vulnerability to spoofing attacks. A new model has been introduced that achieves better performance by combining several biometric features like fingerprints, facial recognition, and physiological data. Results show that the suggested system performs much better than other systems under comparison with 99.92% accuracy and improved adaptability to various types of real-world challenges. The results also highlight how multimodal systems can function as a dependable solution for safe and effective identity verification by overcoming the drawbacks of single-modal techniques.