## 1 Introduction  
Children's learning capacities and psychological development are significantly influenced by the parenting practices and sociodemographic features in their environment. The impact of good relationships, engagement, and participation between parents and children on academic achievement is positive [1]. This engagement involves things like reading aloud to kids, guiding studies, and staying in touch with them, all of which are essential for creating a positive learning environment. The long-term effects of parenting practices such as friendly manners and flexibility significantly influence children's cognitive development and academic performance [2]. Furthermore, different racial and ethnic communities have distinct policies concerning the extent of parental engagement with children, which greatly impacts the growth and development of young children [3]. Sociodemographic factors, such as parental education level and socioeconomic status (SES), also significantly influence children's learning patterns. Moreover, the provision of mental and physical healthcare for children has a direct impact on their brain and cognitive development, highlighting the importance of home environments [4; 5]. Additionally, households of higher SES generally enjoy superior access to educational resources and opportunities as well as good healthcare services, leading to enhanced learning outcomes for children [6]. Researchers are extensively exploring the influence of parenting styles, ensuring privilege, mental and physical care, and awareness of children's learning abilities. Previous studies have demonstrated the application of a variety of methods in child development research. Researchers diagnose mental health problems and analyze parenting techniques to identify learning difficulties. Several research works have examined the application of machine learning (ML) algorithms to predict learning difficulties and variables influencing school-aged children's and college students' academic performance. To diagnose learning difficulties in kids, another study [7] used artificial neural network (ANN) and support vector machine (SVM) classifiers. The study emphasized the innovative application of ML to accurately identify learning disabilities. Researchers used supervised ML algorithms [8] to uncover the reasons behind college students' poor academic performance. That provides advice to help struggling students to do better. Additionally, researchers have used ML approaches to identify mental health issues in children and adolescents, including attention deficit hyperactivity disorder (ADHD) and depression. Multiple researchers [9; 10] have employed ML models to forecast depression and ADHD in children, attaining superior classification accuracy and emphasizing the possibility of timely identification and remediation. Additionally, researchers [11] developed and evaluated ML-based prognosis tools to identify learning difficulties among students, aiming to improve early detection and intervention strategies. Furthermore, researchers used ML models to predict ADHD by analyzing prefrontal brain activity, underscoring the importance of neurophysiological data in diagnostic prediction [12]. Early detection is essential for prompt assistance and intervention in cases of autism spectrum disorder (ASD). ML techniques for early ASD detection have been the subject of several studies [13; 14]. These studies used a variety of classifiers, including random forest (RF), logistic regression (LR), and support vector machine (SVM), to create precise prediction models based on physiological and behavioral data. The results imply that ML methods can improve the accuracy and effectiveness of ASD diagnosis. Recent advancements in ML have significantly improved ASD detection by using SVM, and LR achieved 100% accuracy for children and 97.14% for adults, respectively, and ANN also showed promise, reaching 94.24% accuracy with optimized hyperparameters [15].