Autism spectrum disorder (ASD) presents unique challenges in education, making it essential to understand the cognitive strengths of individuals with autism. To address this challenge, this study proposes the Neuro-Behavioral Fusion Framework (NBFF), which combines neuro-brain and behavioral data to assess cognitive strengths. The framework extracts key features from both data types, such as memory retention and attention span, using a structured approach. For the neuro-brain data, local convolutional layers and encoders are applied, while behavioral data is processed using long short-term memory (LSTM) networks and encoders. The two datasets are fused through feature fusion and passed through a fully connected layer for prediction. To ensure transparency, Explainable AI (XAI) is incorporated, allowing educators and caregivers to understand the model’s decisions. This work aims to create personalized educational plans that improve learning outcomes and promote more inclusive environments for students with ASD. Additionally, this framework seeks to bridge the gap between advanced AI methodologies and practical educational strategies for individuals with ASD.