A challenge prevalent in the photovoltaic (PV) system applications in the domain of control engineering lies in the formulation and refinement of their models. Among the esteemed configurations in this domain, the photovoltaic-thermal (PVT) system emerges as particularly noteworthy. Furthermore, the appeal of employing a modeling approach with minimal complexity is noteworthy in this context. To achieve this objective, there is a growing trend in utilizing machine learning (ML) approaches, known for their data-driven modeling capabilities and minimal complexity. In this paper, the application of the multivariate polynomial regression, a straightforward ML method, has been employed to model the PVT system. This choice aims to address the challenge of model complexity inherent in conventional mathematical method, enabling its utility for control purposes. The proposed method is implemented using empirical data derived from laboratory PVT setups, incorporating geographic inputs situated in Sion, Switzerland. A comprehensive comparison with the conventional method has been undertaken for evaluation purposes. The results, encompassing electrical and thermal powers of the PVT system, indicate that the proposed method achieves significantly greater accuracy compared to the conventional.