<p>Autism spectrum disorder (ASD) is a complex neurodevelopmental condition associated with disrupted brain connectivity. Traditional graph-theoretical approaches have been widely employed to study ASD biomarkers; however, these methods are often limited to static topological measures and lack the capacity to capture spectral characteristics of brain activity, especially in multimodal data settings. This limits their ability to model dynamic neural interactions and reduces their diagnostic precision. To overcome these limitations, we propose a Graph Signal Processing (GSP)-based framework that integrates spectral-domain features with topological descriptors to model brain connectivity more comprehensively. Using publicly available fMRI and EEG datasets, we construct subject-specific connectivity graphs where nodes represent brain regions and edges encode functional interactions. We extract advanced GSP features such as Graph Fourier Transform coefficients, spectral entropy, and clustering coefficients, and combine them using Principal Component Analysis (PCA). These are classified using a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The proposed model achieves 98.8% classification accuracy, significantly outperforming prior multimodal GSP studies. Feature ablation analysis reveals that spectral entropy contributes most to this improvement, with its removal resulting in a nearly 30% performance drop. Additionally, a 25% sparsity threshold in graph construction was found to maximize both robustness and computational efficiency. These findings demonstrate that incorporating frequency-domain information through GSP enables a more discriminative and biologically meaningful representation of ASD-related neural patterns, offering a promising direction for accurate diagnosis and biomarker discovery.</p>