In this study, we established a rigorous protocol to extract perovskite structure from open data sources. We then systematically assessed a variety of ML models in predicting the E f and E g of perovskite materials. Three diverse datasets employed for model training were sourced from three distinct perovskite databases with structures ranging from 1,000 to 20,000 entries. Through benchmarking CML algorithms against GNN-based models across three datasets, we recognized the GATGNN model as the top performer, balancing exceptional prediction accuracy and computational efficiency. Furthermore, we investigated the impact of data size on model performance by varying the training data volumes, emphasizing the need for over 1000 data points for optimal prediction accuracy. We further conducted SHAP value analysis to provide insight into the interpretation of CML models in the decision-making process. Our study provides a standardized benchmark for evaluating various ML models across diverse datasets of perovskite materials, facilitating future applications in materials science, particularly in model selection and the advancement of perovskite materials.