Biometric systems have become crucial in enhancing the security and reliability of identification processes. Among these, iris recognition has gained notable attention due to the unique and stable patterns of the human iris. However, ensuring high identification accuracy while optimizing the storage requirements remains a challenge for researchers. In this article, an iris recognition system that leverages the Wavelet transform technique to extract the most relevant information from the iris image while reducing the storage needs and improving the overall performance of the system, will be introduced. The paper will provide as well a comprehensive overview of iris recognition systems, highlighting previous research and methods employed in the field. Moreover, a summary of the proposed system for iris identification will be presented, emphasizing the feature extraction sub-stage, where the Wavelet transform plays a pivotal role, and detailing the performance achieved. The findings will demonstrate the effectiveness of the applied technique for features extraction which, combined with robust methods for segmentation and classification namely the Hough transform and the Ensemble Learning approach, improves the accuracy and robustness of iris recognition, contributing to the evolution of biometric authentication technologies.