In today's digital era, efficient and secure attendance monitoring is crucial for organizations, educational institutions, and workplaces. Traditional attendance systems, such as manual registers and RFID-based methods, are prone to fraud, manipulation, and inefficiencies. The system employs a CNN-based deep learning model to detect and recognize faces in real time, ensuring high accuracy and robustness against spoofing. The attendance records are then stored in a blockchain ledger, which guarantees tamper-proof, decentralized, and transparent record-keeping. By integrating smart contracts, the system ensures automated and immutable attendance tracking, reducing administrative overhead and eliminating proxy attendance. This approach enhances security, scalability, and efficiency, making it suitable for academic institutions, corporate environments, and government offices. Experimental results demonstrate that our proposed system achieves high accuracy in facial recognition while maintaining data integrity and security through blockchain implementation.