We developed the machine learning model incorporating process information curated from the Perovskite Database. The predictive accuracy improved by considering process information, and the interpretation of the trained RF model confirmed the model's validity, consistent with experimental findings. We clarified the relative importance of the material and process information of each layer, suggesting the significance of considering process conditions for experimental material development and machine-learning prediction. Despite the effectiveness of machine learning, we found that there is a limitation due to data degeneracy. Data degeneracies exist in the original database, affecting machine learning negatively. The balance between data degeneracy and measuring closeness was regulated by split and complement method, identifying that the best representation was 1st split and zero complements. The findings of this work will contribute to the data-driven development of perovskite solar cells.