Another crucial aspect of building accurate models for predicting the properties of OPV devices is the choice of ML algorithm. Key considerations include whether the algorithm is appropriate for the task at hand (e.g., supervised, unsupervised, or reinforcement learning; classification or regression), the size of the dataset, whether information about the model's uncertainty is required, the algorithm's computational cost, and its interpretability. In this context, we evaluated eleven ML algorithms, which can be classified within five broad categories: non-parametric, linear, kernelbased, tree-based, and deep learning (DL). Each algorithm has its own strengths, limitations, and assumptions. The choice of algorithm depends on the specific characteristics of the dataset, the nature of the problem, and the desired output. Researchers should carefully evaluate and select the most suitable model for their specific application in the context of organic photovoltaic devices.