The education technology sector has undergone significant transformation in recent years, necessitating advanced engineering solutions to address emerging challenges. This technical review explores the dual requirements facing contemporary EdTech platforms: delivering highly scalable systems capable of supporting massive concurrent user populations while simultaneously providing sophisticated, AI-powered personalization capabilities. The document examines crucial architectural approaches that enable reliable performance at scale, including service-oriented architectures, multi-tenant designs, and high-concurrency engineering techniques. These foundational patterns are explored alongside the intelligent features they enable—including adaptive learning algorithms, natural language processing applications, and assessment technologies. The integration of these capabilities presents complex challenges requiring carefully balanced design decisions that consider both computational demands and user experience requirements. Future directions in EdTech platform development are also addressed, including edge computing implementations, privacy-preserving machine learning techniques, and interoperability standards for educational data. Through comprehensive exploration of both architectural foundations and AI capabilities, this review provides valuable insights for EdTech developers seeking to create next-generation educational platforms that deliver truly personalized and engaging learning experiences while maintaining exceptional performance characteristics under variable load conditions.