Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting social interaction, communication, and behavior. Autism is the third most common developmental disorder in the world. In India, the prevalence of autism is increasing and is estimated to be around 1 in 68 children. With its rising prevalence, early detection is crucial for timely intervention. This paper serves as both a review and a research study. The review explores existing ASD detection methodologies, highlighting machine learning approaches such as multinomial logistic regression (MLR), support vector machines (SVM), and convolutional neural networks (CNN), along with tools like eye-tracking and EEG analysis. The research component applies machine learning models—including Logistic Regression, Decision Tree, Random Forest, SVM, KNeighbors, Naive Bayes, and Neural Networks—on AQ10 survey data (1054 samples, 19 features) to evaluate their effectiveness. SVM achieved the highest accuracy. Further analysis examined the necessity of all 10 AQ10 questions, revealing that AQ4 and AQ10 contribute the least to predictive accuracy. Heatmap analysis confirmed weak correlations with the total ASD score. These findings suggest that refining ASD screening tools by removing less informative questions can improve efficiency while maintaining diagnostic reliability.