Recent studies suggest that atypical neural function, due to atypical neural structure, is associated with the behavioral symptoms of Autism Spectrum Disorder (ASD). Additionally, studies suggest that the atypical neural functions and structures associated with ASD change from early childhood to adulthood. This study is the first to develop a multiclass classification model to differentiate neural activity patterns of children and adults with and without ASD depicted by their EEG waveforms. In contrary to the conventional binary classification approaches used in state-of-the-art literature, the multi-class approaches learn the similarity, dissimilarity, common and differentiating patterns among all the categories present in the data. We collected 6 minutes of non-invasive resting-state EEG signals from 105 individuals that include ASD children and adults as well as typical children and adults. Since conventional supervised learning multi-class classifiers suffer from overfitting on limited clinical data, this study employed a few-shot learning mechanism, named prototypical network learning, that is adaptive to limited data and robust against data imbalance issues. Our developed model achieved 85% accuracy in multiclass classification. As the next step, we are developing an interpretable machine learning adaptation for prototypical learning to interpret the model inferences and highlight the brain wave patterns indicative of ASD in different stages of development.