A neurological disorder influencing a person’s mental, social, and physical health, autism spectrum disorder (ASD) can afflict people of any age. Due to the disorder’s sophistication and the time-consuming and expensive nature of conventional diagnostic techniques, diagnosing it can be difficult. On the other hand, machine learning methods present a viable path toward smarter and more effective diagnosis. This study compares different machine learning methods, such as Logistic Regression, K-Nearest Neighbor, SVM, and Naïve Bayes, for predicting the incidence of ASD. It also focuses on discovering particular characteristics for automating the diagnostic procedure. Comparing the Naïve Bayes method to other algorithms, experimental study shows that it produces greater accuracy, reaching 99.6%.