An Enhanced Efficient English Learning System based on Artificial Intelligence Driven B/S Architectures

English learning continues to be an important part of global education systems, particularly in non-native English-speaking countries, as there is increasing demand for affordable, interactive, and scalable learning systems. Most established English learning systems are dependent on single-desktop applications or instructor-based approaches, which lack the flexibility, scalability, and interactivity needed. This research presents an effective adaptive English learning system developed with Browser/Server architecture and enabled PCA(Principle Component Analysis) and Bi-LSTM(Long Short Term Memory) to address these challenges. The suggested system proposed utilizes up-to-date web technologies and AI techniques to create a light-weight, easily accessible, platform-independent learning atmosphere. The system uses dynamic delivery of content, in-progress tracking in real time, interactive components such as vocabulary quizzes, pronunciation practice, grammar checking tools, and feedback mechanisms. In comparison to previous systems with limited real-time interaction and cross-device support, the proposed AI driven B/S-based architecture improves system performance, user experience, and deployment ease. Experimental findings from user testing show higher learning participation, better accuracy scores of 96.8%, and lower response time in interactive activities.