## 1 Introduction
Additionally, a study analyzing toddlers using six classification models, including LR, reported a perfect accuracy score of 100% in ASD detection [16]. Beyond ASD detection, research has also examined the influence of parenting practices and other factors on children's development. Studies highlight that parenting style, gender, and socioeconomic background play a significant role in academic achievement, with authoritative parenting being particularly crucial for fostering successful educational outcomes [17]. Again, comparative studies have investigated the effectiveness of ML techniques in predicting children's educational activities compared to traditional methods [18; 19]. These analyses highlight the benefits of ML algorithms for identifying important factors and improving prediction accuracy. The Bangladesh Bureau of Statistics and UNICEF conducted the Multiple Indicator Cluster Surveys (MICS) in 2019. This survey collected a comprehensive dataset that includes information on child welfare, household characteristics, and parenting practices in Bangladesh [20]. Employing this information provides an opportunity to explore the multifaceted relationships between the sociodemographic characteristics, parenting styles, and educational achievement of Bangladesh children, which can then be applied to similar studies. In addition, to analyze and forecast a wide range of outcomes regarding child development and education, ML approaches have become an increasingly powerful tool [21; 22]. Researchers have the potential to use ML algorithms on the MICS 2019 dataset to gain valuable insights into the factors affecting children's learning skill comparisons. Moreover, they can apply these insights to shape interventions and policies that aim to enhance opportunities for children in Bangladesh. The literature review shows how ML can predict academic performances, learning difficulties, mental health illnesses, and parenting styles in child development studies. These studies highlight the complicated relationship between individual, familial, and environmental influences on children. Our research is motivated by the critical need to address and understand challenges in early childhood development and education using advanced computational techniques. Our literature review revealed a research gap, as no previous studies have concentrated on identifying and forecasting the influencing factors for learning difficulties among Bangladesh children. While ML has been widely used in child development studies in other countries, such as Oman, Australia, and the U.S., there has been limited exploration of early childhood learning outcomes in the Bangladesh context. Our study addresses this gap by applying ML techniques to the MICS 2019 dataset, which uniquely captures the influence of sociodemographic factors, parenting styles, and children's learning abilities in Bangladesh. This research highlights the importance of leveraging local data to uncover insights specific to Bangladesh's context and social landscape, providing the foundation for targeted interventions. In addition to applying ML techniques, our study integrates explainable AI (XAI) methods like SHAP and LIME, which offer a novel transparent approach to understanding early childhood learning difficulties in Bangladesh. By incorporating XAI methods, our approach not only improves the interpretability of ML models but also identifies underlying causes of learning difficulties. These findings offer valuable insights for policymaking to improve early childhood education. The highlights of our objective are as follows:  
* Employ statistical analysis to investigate the key factors influencing early learning among children under the age of five;
* Apply different ML models to forecast learning difficulties in children based on the selected features;
* Utilize XAI methods like SHAP and LIME to explain how different factors influence children learning difficulties. These insights will help guide policies to improve education and overall development in early childhood.