Research indicates that early engagement in therapies may be effective in reducing the manifestations of autism spectrum disorder (ASD), despite the absence of a cure for the condition. We used feature modification techniques such as logarithms, Z-scores, and sine values to modify datasets including data regarding autism spectrum disorder diagnoses across infants, kids, adolescents, and adults. In the subsequent step, the modified ASD datasets were used to test further classification techniques. Our findings demonstrated that the regression model exhibited the highest performance when it was applied to the dataset including toddlers. In contrast, Adaboost was the one that provided the greatest service for the adult dataset, while Glmboost was the one that provided the best service for the children’s dataset. Z-score feature adjustments generated the most accurate classifications for the newborn dataset, while sine function feature alterations produced the best results for the children and adolescents dataset. Both datasets were analyzed. Multiple feature selection approaches were used on these datasets, which had been converted into Z-scores, to determine what characteristics are most associated with the development of Autism Spectrum Disorder (ASD) in different age groups. These datasets included toddlers, children, and adolescents. The findings from these analytical approaches show that algorithms for artificial intelligence might potentially produce accurate assessments of the state of autism spectrum disorder (ASD). Therefore, the optimism that simulations have rekindled interest in the potential of diagnosing autism spectrum disorder early on.