Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interaction, communication, and behavior. Early detection of ASD is critical for timely interventions, yet accurate diagnosis is hindered by the overlap of ASD symptoms with other neurodevelopmental disorders. Recent advancements in machine learning have opened new avenues for the early identification of ASD through predictive models that analyze diverse datasets, including behavioral, genetic, and physiological data. This study investigates the efficacy of machine learning algorithms, such as Logistic Regression (LR), Support Vector Machines (SVMs), Naive Bayes (NB), and XGBoost, in ASD detection. Employing robust preprocessing techniques, feature selection methods, and tools like Random Over Sampler to address class imbalance, our models achieved fairly good accuracies compared to recent literature. The findings underscore the potential of machine learning as a transformative tool for ASD screening, enabling precise, scalable, and non-invasive diagnostic solutions. Future research directions include expanding datasets for broader generalizability, integrating neuroimaging data, and exploring the ethical implications of deploying these technologies in clinical settings.