Autism Spectrum Disorder (ASD) entails distinctive challenges in the domains of social interaction, communication, and repetitive behaviors. This paper delves into recent advancements in ASD detection methodologies, reviewing diverse models and techniques including machine learning and deep learning approaches. The study explores the potential of resting-state fMRI, EEG signals, video analysis, and sensor-based technologies for accurate classification and screening. A comprehensive dataset from the University of Arkansas is employed, encompassing 27 features related to autism-related conditions. The proposed methodology involves meticulous data preprocessing, segmentation, and feature selection, utilizing Logistic Regression, Extra Trees Feature Importance, and Recursive Feature Elimination. Various classifiers, including K-Nearest Neighbors, Decision Trees, Support Vector Machines, and ensemble methods, are employed, and evaluated for performance. Results showcase competitive accuracies ranging from 85 to 89.9%, with Bagging emerging as a top-performing classifier and highlighting the impact of hyperparameter tuning on model performance. The findings contribute to the evolving landscape of ASD research, emphasizing the importance of advanced technologies.