Mastering English grammar is a foundational component of language proficiency. However, traditional instruction methods often rely on static exercises and rote memorization, leading to low learner engagement and suboptimal knowledge retention. This thesis presents a novel Artificial Intelligence (AI) based Graphical Learning Interface designed to enhance English grammar acquisition through interactive visual learning, real-time natural language processing (NLP), and adaptive feedback mechanisms. The proposed architecture integrates an NLP engine for immediate error detection, a Machine Learning (ML) based adaptive module for personalized curriculum pacing, and a dynamic graphical user interface (GUI) supporting drag-and-drop semantic construction. A four-week experimental study involving 20 intermediate English as a Second Language (ESL) learners was conducted to evaluate the platform’s efficacy against traditional learning modalities. The results demonstrate a statistically significant improvement in grammatical proficiency, with the experimental group’s test scores rising from 68% to 85%, compared to a modest 70% to 75% improvement in the control group. Furthermore, behavioral analytics extracted from the platform revealed a 10–20% increase in sustained student interaction and voluntary practice rates when utilizing the AI-driven graphical interface. This research provides empirical evidence that coupling intelligent, context-aware tutoring systems with interactive visual interfaces substantially bridges the gap between passive grammar instruction and active, learner-centric education.