English Language Anxiety represents a prevalent issue among learners, significantly affecting their capacity to communicate and acquire knowledge effectively. The study uses machine learning models to offer insights into how anxiety affects students’ general well-being and academic performance. English language anxiety among rural high school students in Bangladesh is investigated using machine learning techniques to determine its sources and effects on academic performance. Machine learning techniques like k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting were used to analyze data from several rural schools, including student scores and qualitative insights on their perceptions of English. The results showed that SVM was the most accurate model, with an accuracy of 99.43%. The study reveals major anxiety patterns and predictions, with a focus on the interaction between academic pressure, English complexity, and student mental health. The findings will guide educators and policymakers in designing focused interventions to relieve English subject anxiety and establish a supportive learning environment.