The Autism Spectrum Disorder (ASD) diagnosis and detection in its initial stages is a more complex issue in the face of the wide-ranging, diverse nature and causes. Subsequent literature inclined towards a possible correlation of gut microbiome with ASD, and its disclosure presents a more promising attribute for imminent discovery conduits. The dataset on gut microbiome associated with ASD focuses specifically on the microbial compositions obtained through 16S rRNA sequencing. This study presents a novel method that integrates Artificial Intelligence employing various Machine Learning (ML) robust classifiers such that Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN), additionally PCA and k-means clustering is implemented for feature extraction to reveal important hidden patterns of ASD associated microbiomes from microbiome profiles. By integrating these model classifiers, the ensemble technique was developed to harness the strengths of each model, which enhances the dependability of the gut microbiome and offers a novel approach. The ensemble method suggested has an accuracy of 98.75%, a precision of 95.11%, a recall of 96.47% and an F1 score of 98.28% in the early determination of autism. The observational feature of this multifaceted approach not only enhances accuracy and precision but also provides a more complete picture of the role of autism spectrum disorders and eventually leads to the development of interventions and personalised approaches to these problems.