Iris identification is the most precise and consistent form of biometric verification because of the uniqueness and consistency of iris patterns for each person. This research presents an advanced iris recognition system that uses a systematic pipeline to provide fast and robust verification of identification. The process begins with capturing an iris image through a near-infrared camera, defining its inner and outer boundaries using image processing techniques like Circular Hough Transform (CHT) and edge detection. The segmentation phase includes iris normalization, which transforms the segmented iris from a circular shape into a normalized rectangular form. This transformation is achieved using Daugman's Rubber-Sheet Model. The pre-trained EfficientNet-B7 deep learning model is used for feature extraction with-out training. The iris image is passed through multiple convolutional layers, activation functions, and feature aggregation operations to create a 1 × 35840-dimensional feature vector that acts as the iris biometric signature. The Cosine Similarity algorithm matches the extracted features against iris templates stored in a database. The method achieved high match accuracy with great precision and reliability in biometric identification, making it a worthy contender for security operations demanding accurate and reliable identification confirmation. The research enhances biometric authentication systems using deep learning and sophisticated image processing techniques, presenting a safe and effective technique for iris-based identification.