Artificial Intelligence (AI) is increasingly transforming higher education by enabling data-driven product management, intelligent decision-making, process automation, and adaptive institutional services. Artificial Intelligence (AI) is transforming higher education from data to decisions, automation to personalization, and services to learning. While organizational influence and AI's predictive power are vital in the process of achieving digital transformation, it is important to understand their significance. The present study is aimed at studying the applications of AI in higher education for product management through the multidimensional approach and machine learning techniques. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the relationships between AIenabled product management capabilities and digital transformation outcomes, while machine learning is used to analyze the institutional transformation patterns and predictive performance. AI product strategy, decision quality, institutional agility, digital governance, and digital transformation are closely interconnected areas, as revealed by the structural analysis, where the organizational capacities related to AI products are crucial for institutional transformation. The overall classification accuracy of the machine learning assessment is 97.5%, and in the categories of institutional transformation, the precision, recall, and F1 scores are all of good predictive performance. Experimental analysis results are also presented by the connection between the features of the institution, the learning behavior, and the confusion matrix to estimate the reliability of the model and the capability of the model to predict the learning behavior. The findings suggest that a combination of explanatory structural modeling and predictive machine learning has the potential to be a comprehensive solution to the challenge of evaluating the transformation of higher education brought about by AI. The recommended approach provides practical recommendations for academic administration, technology leaders, and product managers to enhance institutional decision-making, resource utilization, digital maturity, and approaches to AI transformation.
