The increasing challenges of proxy attendance, manual errors, and data tampering in conventional student attendance systems highlight the need for a secure and automated solution. The current research proposes the design and development of a Blockchain-Based Student Attendance System that integrates facial recognition with decentralized storage. The system employs HOG (Histogram of Oriented Gradients) for face detection, CNN (Convolutional Neural Network) for feature extraction, and KNN (K-Nearest Neighbors) for classification, ensuring accurate identification of students. Once attendance is verified, records are securely stored on a Hyperledger Fabric blockchain, providing immutability, transparency, and tamper-proof management. A web-based interface allows real- time monitoring for faculty, students, and administrators, reducing administrative workload while enhancing trust and accountability.