Machine learning is a multidisciplinary study area that makes use of intelligent approaches to identify useful hidden patterns that get used for prediction purpose to enhance deciding ability. Hence, the increasing use of machine learning models in predicting different human illnesses has made it feasible to identify them early by analyzing numerous health and physiological parameters. This reason encouraged us to look more closely at the identification and evaluation of ASD, which is a behavioural disorder that hinders language and communication acquisition, through Machine Learning models. It helps to develop more effective treatment strategies. As it is very difficult for a practitioner to pinpoint the key characteristics that contribute to an accurate ASD prognosis, an automated technique is required. Additionally, it is possible to generate the most influential characteristics for accurately and promptly predicting ASD through our proposed hybrid approach of explainable AI, along with machine learning algorithms. Thus, the suggested framework provides suggestions for expected outcomes along with a more accurate prognosis, which will be a crucial therapeutic help for better and earlier diagnosis of ASD features of child, toddler, adolescent or adult patients with disorder.