Autistic disorder (ASD) manifests as a neurological and developmental condition marked by difficulties in social interaction and communication, noticeable impairments that are distinctive, and the presence of repetitive behavioral patterns. Early diagnosis holds significant potential to mitigate its severity and long-term impacts. The primary goal of the system is to use a vast range of machine learning methodologies within ensemble learning to train datasets for predictive ASD diagnosis. Through the employment of machine learning strategies, the project seeks to upgrade the competence of autism diagnosis while assessing the accuracy of these algorithms utilized for the classification of individuals who might exhibit indications of ASD. The system enables ASD prediction using diverse patient datasets, encompassing over 10,000 records across various demographics such as children, adults, elders, and transgender individuals.