The rapid expansion of digital education ecosystems has transformed the way student learning behaviors are recorded, analyzed, and interpreted. Traditional academic performance prediction models rely primarily on structured academic indicators such as grades, attendance, and examination scores. However, these models often fail to capture deeper psychosocial and self-regulated learning (SRL) constructs that significantly influence student success. In recent years, data-driven methodologies inspired by control systems engineering, statistical process monitoring, and machine learning have provided new opportunities to develop more comprehensive predictive frameworks. This paper proposes a conceptual integration of psychosocial constructs and SRL behaviors with advanced data-driven modeling techniques derived from fault detection, system identification, and adaptive control theory. Drawing parallels between industrial process monitoring (Qin, 2003; Ding, 2014) and educational data streams, the study develops an intelligent framework for academic performance prediction. The model incorporates subspace identification methods (Overschee & De Moor, 1996; Qin, 2006), randomized algorithms (Tempo et al., 2005), and reinforcement learning approaches (Lewis et al., 2012) to construct adaptive predictive systems. The paper further explores how psychosocial indicators such as motivation, engagement, and self-regulation can be mathematically represented within a data-driven learning architecture. Finally, challenges and future directions for explainable, scalable, and real-time academic prediction systems are discussed.