Autism Spectrum Disorder (ASD) is a complex, lifelong neurodevelopment disorder that affects how individuals with the disorder interact with and perceive their environment. In recent years, machine learning has been increasingly used to aid in the diagnosis and prediction of ASD.This paper presents a machine learning technique for predicting autism spectrum disorder (ASD) based on a combination of behavioral and medical features. The model was built using a large dataset of both ASD and non-ASD individuals, along with a variety of features such as age, gender, medical history, and behavioral traits. A decision tree-based classifier was used to develop a predictive model, which was then tested on a separate test set. The results showed that the model was able to accurately classify ASD individuals with an accuracy of 94% and a precision of 95%, outperforming the baseline accuracy of 82%. The results suggest that this model could be used to accurately identify individuals with ASD, thus improving ea Reinforcement Learning diagnosis and care for those affected. Finally, using customized reinforcement learning could provide a way to optimize the performance of a predictive model over time by learning from its decisions and adjusting its parameters accordingly