Autism Spectrum Disorder is a neurodevelopmental disorder characterized by the persistence of deficits in communication, social interaction, and restricted and repetitive patterns of behavior, interests, or activities. Early and reliable identification—particularly before age three years—is of prime importance to enable timely interventions that can considerably enhance long-term outcomes. However, the traditional machine learning paradigm falls short while managing high-dimensionality, multi-modality, and the interpretation requirements of ASD datasets. In this paper, the authors propose a quantum–classical hybrid learning architecture for early diagnosis of ASD. This framework integrates VQCs and QNNs together with classical feature extractors, such as CNNs and ANNs, for the purposes of constructing stable multi-modal representations. Neuroimaging data (MRI/fMRI), genetic information, such as SNP/gene expression, and behavioral measures are combined into a common diagnostic workflow with quantum kernel-based classifiers at its heart. Early feature attention has been introduced to bring forth predictive biomarkers arising well before actual clinical diagnosis. The implementation and simulation of quantum parts will be performed using the PennyLane and Qiskit (IBMQ/Aer) environments. Performance will be benchmarked against the results from classical baselines, XGBoost, SVM, and CNN, by accuracy, precision, recall, F1-score, and ROC-AUC, while interpretability will be assessed by SHAP and QSHAP analyses. This work aims to emphasize the feasibility, interpretability, and diagnostic superiority of quantum-enhanced learning systems for ASD prediction and subsequently work toward developing scalable, explainable, and personalized clinical decision-support tools.