A neurodevelopmental disorder dyslexia has a global impact on millions of children with sloppy word comprehension and subpar reading abilities. This affects their academic, emotional, and social well-being. Early detection of dyslexia can be highly beneficial for dyslexic children as their learning needs can be properly addressed. Machine learning methods have been implemented to recognize dyslexia with various datasets acquired from medical and educational organization. An innovative approach to early dyslexia detection by employing ensemble machine learning method, such as Logistic Regression (LR), Random Forest (RF), Nearest Neighbors (KNN), and a Support Vector Machine (SVM). In this research indicates that all models exhibit commendable performance, with LR model achieving the highest accuracy 89.59%, followed closely by KNN and RF models. Likewise, SVM model demonstrates a commendable accuracy rate of 86.58%. With an impressive 91.50% accuracy, the ensemble model, this outperforms individual models and demonstrates the efficacy of ensemble method.