ASD is a highly complex neurodevelopmental disorder with social interaction and communication challenges that diagnosis, in most cases, involves laborious behavioral analysis and can delay much-needed early interventions. The following paper introduces the use of ML in the detection of ASD using models such as Logistic Regression, AdaBoost, and ensemble approaches like XGBoost and Random Forest at an extremely high accuracy rate. It has been recognized that current methods toward data privacy are short of the mark using federated learning (FL), our solution has enabled model training locally in decentralized devices that guarantee patient confidentiality. Since there’s an aggregation of updates rather than actual raw data, there is therefore a higher preservation of privacy while maintaining efficiency at collaborative learning. This experiment shows its FL-based metaclassifier with ASD-specific data with higher diagnostic accuracy, a better model, and significantly greater advancements in safe accurate early detection strategies for ASD.