## 1 Introduction  
Science, Technology, Engineering, and Math (STEM) fields are vital for driving a nation's economic growth and ensuring sustained development. These disciplines are crucial not only for fostering innovation but also for maintaining global competitiveness. Despite growing interest in STEM fields, in recent years we have witnessed a concerning trend of declining enrollment and rising dropout rates. This attrition poses significant challenges, impacting not only individual students but also their families, schools, and broader national progress. As students leave STEM programs, there is a loss of future professionals in key sectors, which can stifle scientific and technological advancements [1, 2]. To address this challenge, emerging technologies like machine learning (ML) and big data analytics offer promising solutions. Machine learning leverages historical data, including information on student dropouts, while big data refers to the rapid and vast growth of diverse data sources [3]. These technologies can be used to develop predictive models capable of identifying students at risk of dropping out. Such models enable early detection, allowing educational institutions to implement timely interventions. Research has consistently shown that the first year is the most critical period for student retention, with a significant percentage of dropouts occurring during this time [4]. STEM education itself can be defined in various ways depending on the perspective. [PERSON] and [PERSON] [5] describe STEM as a teaching methodology that integrates core scientific and mathematical principles with practical technological and engineering concepts [6]. [PERSON] further emphasizes STEM as an interdisciplinary approach that promotes the exploration of the interconnectednessbetween these subjects. [PERSON] and [PERSON] take this a step further by advocating for a standardized, cross-disciplinary instructional approach, where content is taught interactively to promote student engagement [7]. However, beyond these definitions, the key focus remains on STEM literacy. [PERSON] suggests shifting from simply imparting STEM knowledge to fostering \"STEM literacy\", which involves continuously applying learned skills to new and evolving challenges [8]. This evolving definition underscores the growing importance of cultivating not just STEM knowledge but the ability to apply it in real-world contexts. The decline in STEM enrollments and increasing dropout rates carry far-reaching implications for the economy, innovation, and societal progress. Big data has emerged as a crucial tool in solving complex, multi-faceted challenges across various sectors, including education, healthcare, engineering, and environmental science [9; 10; 11; 12; 13]. It allows researchers to uncover hidden patterns, correlations, and relationships within vast datasets, fundamentally revolutionizing research practices. Machine learning [14; 15; 16; 17], a subset of artificial intelligence (AI), complements big data by making data-driven predictions and is already being applied in diverse fields such as the Internet of Things (IoT) and biomedical engineering [18; 19; 20; 21; 22]. This study capitalizes on the synergy between big data and machine learning to identify key parameters that influence student dropout rates, develop a predictive model, and enable real-time monitoring of student progress. The overarching goal is to allow educational authorities to intervene before students disengage entirely. Using historical data from past student cohorts, the project employs Python's random forest algorithm to identify critical factors leading to dropout. This model helps educators understand why specific students are at risk, thus allowing for more personalized interventions. One significant factor identified by the model is the socioeconomic background, where students from less privileged households are disproportionately more likely to drop out compared to their peers from more affluent families.