Solar power forecasting is essential to maximizing the integration of renewable energy sources and guaranteeing effective grid management. The application of explainable artificial intelligence (AI) and machine learning for solar power forecasting is covered in detail in this chapter. Artificial neural networks (ANN), support vector machines (SVM), random forest, and gradient boosting are some of the AI models that have been investigated. These models were selected due to their capacity to recognize complex patterns and nonlinear correlations in solar energy data. The preprocessing of the data, feature selection, model training, and evaluation are the phases that make up the forecasting process. To improve performance, proposed architecture of XAI-ML-based solar energy forecasting has been discussed.