Several machine learning classifiers have been used for Autism Spectrum Disorder screening, however, literature in finding the best classifier for this application domain is inadequate. Hence, this paper presents a comparison of five (5) supervised machine learning algorithms: Decision Tree, Naïve Bayes, k-nn, Random Tree, and Deep Learning using small datasets (n=1100) on child, adolescent and adult ASD screening in finding the most appropriate classifier. These algorithms, which are evaluated using a broad set of prediction performance metrics including accuracy, precision/recall measures, and Receiver Operating Characteristics, are compared against each other. The experiment result suggests that the Deep Learning classifier gives the best performance (with more than 96%) in almost all metrics while the Random Tree classifier came out as the least performing classifier in all the performance metrics.