This study presents a comprehensive framework for engineering education that integrates analytical and data-driven modeling with machine learning (ML) methods, numerical simulation, hardware-in-the-loop testing, and hands-on experimentation to deepen students’ understanding of dynamic engineering systems. By applying this methodology to photovoltaic (PV) and photovoltaic-thermal (PVT) systems, the study highlights the efficacy of advanced ML models, such as polynomial regression, autoregressive moving-average or more complex ML-based modeling methods such as long short-term memory, for predicting system outputs such as voltage, current, electrical power and thermal power. Experimental validation demonstrates the superior accuracy and efficiency of the ML models compared to traditional analytical approaches. The results underscore the potential of combining theoretical rigor with practical application to equip students with the skills required for addressing real-world engineering challenges. This educational approach not only enhances technical proficiency but also fosters critical thinking, bridging the gap between conceptual knowledge and practical implementation.