The increasing demand for secure and reliable identity verification systems has accelerated research in biometric authentication, particularly iris recognition due to its distinctive physiological characteristics and high identification accuracy. However, conventional iris recognition approaches encounter challenges related to non-ideal image acquisition, segmentation errors, feature variability, and computational complexity. This research proposes an Intelligent Iris Authentication Framework Using Genetic Optimization for Robust Biometric Identification, integrating genetic algorithm-based optimization with advanced iris feature processing mechanisms to enhance recognition reliability. The proposed framework focuses on improving feature selection, reducing redundant biometric patterns, and optimizing matching performance under variable environmental conditions. The conceptual model combines iris segmentation, feature extraction, genetic optimization, and intelligent authentication decision-making to establish a robust identification pipeline. Existing studies demonstrate the effectiveness of statistical iris analysis, machine learning classifiers, and evolutionary optimization methods; however, limitations remain in achieving adaptive performance across heterogeneous biometric scenarios. The proposed framework addresses these limitations by introducing an optimization-driven authentication strategy capable of improving feature representation and classification efficiency. Furthermore, the study considers human-centered trust and intelligent decision modeling perspectives, emphasizing the importance of reliable AI-assisted authentication systems. The findings indicate that genetic optimization can provide enhanced robustness, scalability, and adaptability for next-generation biometric identification applications.