## 1 Introduction  
Autism spectrum disorder (ASD) affects 1.5% of children worldwide, being 4.5 times more prevalent in males than in females [1]. In 2023, the estimated prevalence of ASD in the United States is 80.9 per 10,000 people, while in Saudi Arabia, it is 100.7 per 10,000 people, reflecting similar patterns observed in many countries [2, 3]. ASD is a developmental disorder marked by early social communication and interaction impairments, along with restricted and repetitive activities and interests [4]. The term \"spectrum\" refers to the variation in the severity and form of symptoms, which were classified as separate disorders before the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [5]. Symptoms appear in early childhood and continue throughout life, resulting in challenges such as learning difficulties, increased psychological pressures on families, and social isolation. The cause of ASD remains unknown, making effective treatment challenging. However, early diagnosis enables healthcare providers and familiesto implement early interventions that enhance the quality of life for individuals with autism. Presently, diagnostic approaches rely heavily on behavioral characterization, which is time-consuming, expensive, susceptible to bias, and often does not meet DSM-5 evaluation criteria. Moreover, due to comorbidity, these approaches may be inconclusive [5]. Structural magnetic resonance imaging (sMRI) is a non-invasive technique to study brain morphology and diagnose disorders, particularly in children. It provides high contrast sensitivity and resolution without radiation [6]. sMRI is used to obtain different brain tissue sequences, such as T1 and T2. Longitudinal studies also utilize sMRI to monitor brain growth over time [7]. Moreover, the big brain theory [8] has a structural basis, positing that autistic people have larger brains than their peers with typical development (TD). However, older statistical methods for studying structural changes in brain regions are univariate and based on group differences, which cannot be directly related to clinical diagnosis based on biomarker identification for an individual [9]. Machine learning (ML) has recently contributed to building efficient computer-aided diagnostic (CAD) systems, analyzing complex medical imaging and Big Data in less time and eliminating human errors. It also facilitates studies at the individual level and acts as a vector with multiple variables [5]. ML models using data can automatically predict and diagnose ASD earlier, improving the lives of patients and their families and reducing financial costs. Personal and behavioral features, along with sMRI data, are valuable resources for ML algorithms in uncovering hidden patterns for disorder prediction. However, challenges arise from hyperparameters and high-dimensional data, making algorithm operation difficult. Optimization approaches like random search methods, feature selection (FS), and hyperparameter tuning can enhance algorithm development, accelerate classification, reduce costs, address high dimensionality, and improve prediction model accuracy [10]. There are three common types of FS algorithm: filter, wrapper, and embedded, which aim to select the most important and effective features for prediction problems. New optimization techniques, such as bio-inspired algorithms, can enhance FS methods by globally searching for the optimal feature subset and improving prediction accuracy [11]. ML has shown significant benefits in accurate ASD diagnosis, saving time and effort for human experts and facilitating effective intervention. Despite recent attention, further improvement is needed in this area, and more research is required for classifying ASD using enhanced algorithms. To our knowledge, this study is the first to employ the nature-inspired grey wolf optimizer (GWO) algorithm and the Boruta algorithm for sMRI-based ASD classification.