The emergence of Artificial Intelligence (AI) has significantly transformed educational methodologies across the globe, particularly in the field of English Language Teaching (ELT). Traditional approaches to language learning often adopt uniform instructional methods that fail to address the cognitive diversity, emotional variations, and neurological learning patterns of individual learners. In response to these limitations, the concept of NeuroAdaptive ELT has emerged as an innovative interdisciplinary framework integrating neuro-pedagogy, cognitive science, and AI-supported adaptive learning technologies. This study explores how AI-driven systems can analyze learners’ cognitive responses, emotional engagement, memory retention, attention span, and linguistic behaviors to create personalized English learning environments. The research adopts a mixed-method approach involving experimental learning sessions, AI-assisted language assessment tools, learner analytics, and qualitative feedback from participants. Findings indicate that neuro-adaptive AI systems enhance vocabulary acquisition, pronunciation accuracy, communicative competence, learner engagement, and emotional confidence. The study concludes that NeuroAdaptive ELT has the potential to redefine future English education by creating intelligent, emotionally responsive, and cognitively adaptive learning ecosystems capable of supporting individualized language acquisition.