Two-dimensional (2D) perovskites are emerging as promising candidates to address the stability issues of three-dimensional perovskites. However, the application of machine learning (ML) to predict the performance of 2D perovskite solar cells is still in its infancy. Furthermore, organic cation spacers in 2D perovskites are often omitted in predictions of solar cell performance. Herein, a large dataset of 2D perovskites, including device and chemical information, is preprocessed for ML applications. Interpretable algorithms and neural networks are applied to predict the performance of 2D perovskite solar cells, yielding R2 values between 0.5 and 0.7. Feature importance analysis from different algorithms shows that the organic cation spacer is one of the most critical features in predicting the performance of 2D perovskite solar cells.