Data-driven machine learning (ML) methods have emerged as a powerful tool to effectively explore the vast compositional space and accelerate the discovery of high-performance perovskite materials. ML models can be classified into two categories, conventional ML (CML) models and deep learning models, according to their architecture, feature representation, complexity, and scalability. CML models typically operate on fixed-size feature vectors, often referred to as descriptors, to unveil the structure-property relationships within the dataset. Therefore, the effectiveness of CML models depends on comprehensive datasets, well-designed feature engineering, and appropriate model selection. For instance, Liu et al. utilized an XGBoost model with eight chemical composition features, including various cations (Cs, methylammonium, formamidinium, Pb, Sn) and anions (I, Br, Cl), to predict the E g and power conversion efficiency of ABX 3 -type perovskites. Similarly, Chenebuah et al. evaluated the accuracy of 12 ML models using 57 features, such as atomic number and electronegativity, along with stability and crystallographic data, to predict the electronic properties of ABX 3 and A 2 BB'X 6 perovskites. Additional experimental data, like UV-vis absorption and J-V curve data, have also been integrated into ML training to enhance predictions of E g and solar cell performance. Despite these advances in perovskite materials applications, limitations persist due to the relatively small size of datasets (fewer than 1500 data points) and the challenges of identifying optimal descriptors for CML models.