Abstract : The rapid evolution of Educational Technology (EdTech) has necessitated the development of intelligent systems capable of tailoring learning experiences to individual student needs. This paper presents the design, architecture, and implementation of a Personalized Study Material Recommendation System embedded within a web-based digital classroom platform called the SMS Digital Classroom. Central to this system is the Student Progress and Performance Analysis AI (SPPAAI), an intelligent analytical engine that monitors student performance across tests, quizzes, and assignments, computes efficiency scores based on difficulty-weighted question categories, and recommends targeted study materials to address identified academic weaknesses. The platform employs a modern full-stack web architecture utilizing React, Vite, Express.js, MongoDB Atlas, Socket.IO, and JWT-based authentication. The system also incorporates Retrieval-Augmented Generation (RAG) methodology to enhance the quality and relevance of recommended study content. This work addresses key challenges in contemporary education, including unequal access to learning resources, lack of adaptive feedback mechanisms, and inefficient progress tracking. The paper outlines the three-phase research lifecycle, technical components, system workflow, mentor feedback, and future development directions.