In this study, we performed a comprehensive evaluation of both CML and GNN based models for predicting E f and E g of perovskite materials. We developed a standardized protocol for extracting and filtering perovskite structures from three databases, i.e., MP, OQMD, and CMR, based on three criteria: the presence of exactly five atoms in the crystal, the occurrence of octahedral motifs, and alignment with established perovskite prototype structures. Our benchmarking of CML against GNN-based models across these datasets revealed that the GATGNN model excelled, demonstrating superior prediction accuracy and computational efficiency simultaneously. Furthermore, we investigated the influence of training data volume on model performance. Notably, the majority of ML models showed significant performance improvements when data volumes increased from 200 to 1,000 entries. Beyond 1000 data points, the performance of all the models stabilized, indicating negligible gains with additional data, as reflected in converged R 2 and MAE values. Additionally, our investigation into feature importance, leveraging SHAP value analysis, confirmed a consistent consensus on significant features within the same dataset and highlighted how data distribution variances critically affect feature selection across datasets. Our study sets a benchmark for evaluating various ML models on diverse perovskite datasets, aiding model selection and advancing perovskite material applications in materials science.