Dyslexia is a neurodevelopmental learning disorder that disrupts a student’s ability to read, write, and spell correctly. The main issue rests on the reliance on manual, diagnostic models that are time-consuming, costly, and often unavailable early on in a student’s education. This is why it takes longer to identify dyslexia and students’ performance is negatively impacted. To help address this problem, this paper proposes an Artificial Intelligence (AI)–based solution that automatically screens handwriting images for dyslexia and provides adaptive educational support. This proposed hybrid model combines Convolutional Neural Networks (CNNs) for automatic feature extraction with Support Vector Machines (SVMs) to effectively categorize handwriting irregularities, including slant, spacing, and baseline deviation. Based on the Kaggle Dyslexia Handwriting dataset, the CNN and SVM models achieved 95% and 92% accuracy, respectively. Explainable AI (XAI) methods, such as Grad-CAM and SHAP, provided explanation to the underlying algorithms, while the Large Language Model (LLM)–based tutoring component assembles individualized reading activities. Two proofs of concept were achieved as the results show that this system represents an efficient, scalable, and interpretable solution to early dyslexia screening and also bridges AI and education for all learners and learning environments.