The aim of this paper is to integrate quantum-inspired algorithms with cognitive and adaptive behavior assessments to advance the early detection of Autism Spectrum Disorder (ASD) in toddlers. Emphasizing the critical need for timely diagnosis and intervention, the paper critiques the limitations inherent in traditional diagnostic practices, which often rely on subjective behavioral assessments and are impeded by resource constraints and cultural biases. By leveraging advanced computational techniques such as Quantum-inspired Particle Swarm Optimization (QPSO) and Quantum-inspired Ant Colony Optimization (QACO), this research seeks to refine feature selection from complex datasets, thereby improving the sensitivity and specificity of ASD identification. The proposed methodology merges these algorithms separately with machine learning classifiers, enabling a more objective, efficient, and accurate screening process for ASD. Through this novel approach, the research aims to provide clinicians with a robust framework for a more thorough understanding of a child’s developmental profile, facilitating earlier and more targeted interventions.