English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized guidance to the students in all aspects. The paper has established an automatic early warning system to monitor the students' psychological state at any time period. The data is collected from 650 respondents from four different public universities in China. The data analysis has been done with the help of powerful SPSS software and the methodology which we used for determining sample size is, Random sampling. The study involves a qualitative assessment to identify participants' characteristics and categorize them to appropriate clusters. The findings of the research showed that the most obvious differences in mental health between students who used automatic warning and those who did not use automatic warning were: depression, anxiety, hostility, terror, and psychosis. The proportion of students who use early warning was less than those who did not use early warning. Research contributes to policymakers to emphasize the importance of incorporating mental health support and resources into educational policies. The novelty of the study seeks to provide a deeper understanding of how AI and big data can optimize mental health education for English students. With the support of AI and Big data there is a constant monitoring and improvement effect on English education students' mental health.