Autism spectrum disorder (ASD) is becoming more and more prevalent in the modern era. Using image processing methods, it takes a lot of effort and money to identify autism characteristics through screening testing. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism at a very young age. Numerous research studies have been conducted using various methodologies, but they have not produced any conclusive findings on the prediction of autism features in relation to age groups. Therefore, the purpose of this study is to design a user interface for predicting ASD in individuals of any age and to offer an effective prediction model based on machine learning techniques. As a result of this study, Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and the CNN algorithm were combined to create a prediction model for autism. A user interface was also created based on the suggested prediction model. The AQ-10 (Autism Spectrum Quotient - 10 item version) is a brief screening tool used to identify whether an individual may exhibit traits associated with Autism Spectrum Disorder (ASD). Autism can be diagnosed at any age, although symptoms typically start to show up in the first two years of life and progress over time. Autism sufferers deal with a variety of issues, including learning deficiencies, concentration issues, mental health issues like anxiety and depression, movement difficulties, sensory issues, and many more. ASD-related data, including brain imaging, facial expression recognition, and a dataset of 250 actual datasets gathered from individuals with and without autistic features, were used to assess the suggested model, false positive rate (FPR), sensitivity, and precision. According to the evaluation findings, the suggested prediction model performs better in terms of accuracy and data specificity.