An Ai Enhanced Asset Register for a school project, the proposed AI-Enhanced Web-Based Asset Management System is designed to streamline the management of institutional assets throughout their lifecycle — from registration and utilization to maintenance and disposal. The system will operate as a secure, browser-based application built using HTML, CSS, JavaScript, PHP, and MySQL, ensuring accessibility, scalability, and responsiveness across devices. Upon logging in, users will access the system according to their assigned roles. Role-based access control (RBAC) will ensure that only authorized personnel, such as administrators or asset managers, can add, modify, or dispose of assets. To enhance data protection, the system will incorporate two-factor authentication (2FA) for sensitive operations such as asset disposal, guaranteeing that all high-level transactions are verified and traceable. Each asset will be uniquely identified through a QR code, generated automatically upon registration. Scanning the QR code will instantly retrieve the asset’s details — including name, category, purchase date, cost, and current depreciation value — simplifying physical audits and authentication processes. The system will also include automated depreciation calculations, allowing real-time estimation of each asset’s current value based on its age and condition. Furthermore, a lightweight artificial intelligence (AI) module will analyze existing data such as depreciation rates, maintenance history, and usage frequency to predict when an asset might require maintenance or disposal. This predictive capability will assist management in making proactive, data-driven decisions. To support accountability and oversight, the platform will feature interactive dashboards and visual reports displaying asset summaries, category distributions, and financial trends. The interface will be designed using responsive web technologies to ensure accessibility on desktops, tablets, and mobile devices. In summary, this system combines automation, intelligence, and security to improve transparency, accuracy, and operational efficiency in asset management. By integrating modern technologies such as AI analytics, QR code identification, and two-factor authentication, the solution represents a modern, innovative, and achievable implementation suitable for an academic project. The Assets reports, depreciation reports and security login details should be csv downloadable