Autism Spectrum Disorder (ASD) is gaining popularity more quickly than ever in recent years. As we all know, ASD is a neurodevelopment condition involving social interaction and behavior issues. Each person’s experience with ASD and level of symptom severity varies. Autism may be diagnosed at any age. According to research, the most common behaviors in people with autism are aggression, self-harm, elopement, tantrums, obsession, and lack of compliance. Therefore, it becomes crucial to identify any indicator that points to severe ASD as soon as possible. Autism spectrum disorder (ASD) is a complex and degenerative neurodevelopmental disorder. There is an urgent need for a timely and readily available diagnostic tool capable of early identification of autism, which can aid clinicians in making early treatment decisions. While there is currently no known cure for autism, early identification allows for more efficient therapy interventions and gives autistic people the ability to live happy, self-sufficient lives. This study used machine learning techniques such as Decision Tree, Random Forest, Naive Bayes, Logistic Regression, K-nearest neighbor, and Support Vector Machine to predict ASD. The user will be able to determine if they have ASD using performance measures, which aid in analyzing the accuracy level of each data with all the applied algorithms to determine which delivers correct results in terms of time and accuracy. The experiments in this work were carried out using the data obtained from publisher Faizu Nabi. The Hybrid model accurately classified Autism spectrum disorder and achieved the highest accuracy of 100%. It proved its superiority over other ML classifiers. In contrast, the other models also showed good performance, and the NB model acquired the best accuracy among the rest of the classifiers of 95%. The experimental results indicate that the suggested method is more effective than conventional methods.