AI-Enabled Intelligent E-Library Ecosystem: A PWA-Based Framework with Predictive Risk Analytics and Dual-Portal Architecture

The evolution of digital learning environments re-quires library systems that move beyond basic catalog automation toward intelligent, user-centric ecosystems. This research intro-duces an Intelligent E-Library Ecosystem developed as a Progres-sive Web Application (PWA) to enhance accessibility, automation, and operational transparency within academic institutions. The proposed system incorporates several advanced features that are rarely unified within conventional library platforms, including a formalized lost-book reporting workflow, real-time seat reservation management, automated due-date reminders, an integrated online PDF reader, a centralized digital notice board, predictive borrower risk analysis, and scheduled email notification services. A secure dual-portal architecture separates administrative control from student interaction, ensuring struc-tured access management and data integrity. A Python–Flask-based machine learning microservice per-forms behavioral risk assessment to support proactive decision-making. Offline functionality is enabled through Service Worker implementation, ensuring continuity of access in low-connectivity environments. Experimental validation demonstrates improve-ments in process efficiency, user engagement, monitoring ac-curacy, and administrative responsiveness when compared to traditional manual and semi-digital library systems.