Autism spectrum disorder (ASD) can be defined as a neurodevelopmental disorder that affects how children interact, communicate and socialize with others. This disorder can occur in a broad spectrum of symptoms, with varying effects and severity. While there is no permanent cure for ASD, early detection and proactive treatment can substantially improve the lives of many children. Current methods to accurately diagnose ASD are invasive, time-consuming, and tedious. They can also be subjective perspectives of a number of clinicians involved, including pediatricians, speech pathologists, psychologists, and psychiatrists. New technologies are rapidly emerging that include machine learning models using speech, computer vision from facial, retinal, and brain MRI images of patients to accurately and timely detect this disorder. Our research focuses on computational linguistics and machine learning using speech data from TalkBank, the world's largest spoken language database. We used data of both ASD and Typical Development (TD) in children from TalkBank to develop machine learning models to accurately predict ASD. More than 50 features were used from specifically two datasets in TalkBank to run our experiments using five different classifiers. Logistic Regression and Random Forest models were found to be the most effective for each of these two main datasets, with an accuracy of 0.75. These experiments confirm that while significant opportunities exist for improving the accuracy, machine learning models can reliably predict ASD status in children for effective diagnosis.