The rise in the prevalence of Autism Spectrum Disorder (ASD) necessitates the employment of early detection and diagnosis to increase the potential for improving interventions. Traditional diagnosis involves the use of subjective behavioral assessments that have a significant variation when it comes to time consumption. The primary contribution of machine learning models is their potential to help automate the detection of ASD. This research explores such potential through the use of the Autism Brain Imaging Data Exchange dataset. The four various types of machine learning algorithms, support vector machine (SVM), recurrent neural network (RNN), random forest (RF), and artificial neural network (ANN), have been used to classify ASD cases. Data preparation involves the imputation of missing values, feature scaling, and removal of outliers and redundant features to ensure the quality and relevance of the dataset in use. The data is split into training, validation, and test sets, and the models will be trained to calculate accuracy, precision, recall, and F1-score. The test results show that the SVM is better compared to other models, with an accuracy of 96.5, followed by RNN at 94.8%, RF at 93.5%, and ANN at 90.0% accuracy. The SVM model presented the lowest data loss; therefore, it should have better generalization capabilities toward unseen data.