Autism Spectrum Disorder (ASD) is a neurological condition impacting more than 1 in 44 children, with increasing prevalence rates. The diagnostic process for ASD can be lengthy and expensive, making early identification of autistic children challenging. Several studies have utilized machine learning techniques to enhance this diagnostic process. However, some have demonstrated low accuracy, while others have employed feature selection to identify the most significant attributes. This research presents a robust model aimed at early autism pr diction using advanced machine learning techniques, which are effective tools for making accurate predictions including all features. The techniques applied in this study include Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). The dataset used to evaluate these techniques was obtained from Kaggle.com. Comparative analysis of these techniques was conducted based on metrics, including accuracy, sensitivity, specificity, precision, F1 score, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Results indicated that Support Vector Machine and Logistic Regression achieved perfect scores.