Autism Spectrum Disorder (ASD) is a set of neurological impairments which are incurable but can be improved with early treatment. We obtained slightly earlier detected ASD datasets pertaining to children and highly processed the dataset as needed. Various ML approaches were applied to the collected dataset and compared their performance based on accuracy, precision, recall, f-measure, log loss, kappa statistics, and MCC. We found that Random Forest and XGBoost provide the best performance with 97.70% accuracy. Model Interpretation methods were applied to interpret the models how they predict positive or negative class. In addition to that important feature and the impact of features on each class been found by shapely value. The impact of features on ASD is found to find the risk factors, which are mostly related or responsible for ASD. The study’s findings indicate that, when properly tuned, machine learning approaches can offer accurate forecasts of ASD status. According to the findings, the suggested model has the ability to diagnose ASD in its early phases.