The urgent need to develop intelligent yet practical domestic security systems has become a critical concern. Traditional door locks provide only physical protection and lack features such as remote access, real-time monitoring, and alert systems. As safety demands increase, smart systems capable of active monitoring, alerting, and responding are becoming essential. This study presents the design of an IoT-based Advanced Smart Lock System to enhance home security through facial and fingerprint recognition, real-time notifications, and role-based access control. Residents can remotely lock or unlock doors, view access records, and assign access based on roles such as household heads, members, or guests. The system was developed using the Spiral Software Development Life Cycle (SDLC), integrating hardware and software in iterative phases. Key components include a Raspberry Pi microcontroller, solenoid lock, fingerprint scanner, Pi camera, sensors, an alarm module, and a keypad. For biometric recognition, the system utilizes MobileNetV2 and DeepPrint on TensorFlow Lite and OpenCV. A cloud interface built with React and Firebase enables remote control and secure data storage, while SQLite supports offline functionality. Initial testing demonstrated accurate biometric identification, secure remote access, proper role-based permissions, and real-time alerts during unauthorized access attempts. The system proved to be user-friendly and effective in enhancing smart home security. As it remains in beta, further testing across diverse environments and with more users is recommended. Future enhancements could include deployment for business use and integration with broader smart home ecosystems.