Artificial Intelligence can play an important role in smart monitoring and supportive housing facilities for patients suffering from neuro-degenerative disorders. Autistic children have challenges with social skills, recurrent activities, verbal and nonverbal communication, and adapting to their surroundings. As a result, dealing with autistic children is a severe public health concern since it is difficult to tell what they are experiencing due to a lack of emotional and cognitive skills. Unfortunately, to date, no proper treatment has been discovered for autism and it is considered an incurable disease. This study focuses on developing cognitive capacity and emotional quotient, as well as enhancing the autistic child’s ability to function and engage constructively in society. To solve this problem we have considered Artificial Intelligence and Internet of Things devices such as mobile and headsets to build a friendly and assistive environment for autistic children. To test the cognitive framework we have used an online publicly available Deap dataset and instead of using one feature selection method, we have used 3 feature selection methods Correlation method, Information Gain, and Recursive Feature Removal method to select the best features in each step. Extreme Gradient Boosting has been used in this framework as a classifier as it is quick and reliable. We have considered it as a binary classification problem and binary class classification has been done in valence, arousal, dominance, and liking scales.