Autism Spectrum Disorder (ASD) is a neurodevelopment condition marked by challenges in social interaction, communication, and repetitive behaviors. Treatment typically involves tailored therapies and interventions to address individual needs. It explores the use of various machine learning techniques, such as AdaBoost, random forest, decision tree, logistic regression, support vector machine, linear discriminate analysis, and a voting classifier, to predict and analyze autism spectrum disorder across different age-groups—toddlers, children, adolescents, and adults. It aims to achieve accurate predictions and early detection of autism spectrum disorder. The results show that the highest accuracies are achieved at 96.6% accuracy and 99.5% precession for the Voting Classifier Toddler subset, 99.5% accuracy for the Voting Classifier Children subset, 99.8% precision and F1-score for the Voting Classifier Adult subset, and 95.2% accuracy and recall for the Voting Classifier-based Adolescents subset by scaling data differently for different age-groups. The proposed framework shows promising results for early autism spectrum disorder detection compared to existing approaches.