•A prediction methodology to predict wind and solar power curtailments in CAISO.•Machine learning-based forecasting using cross-validation and hold-out approaches.•Analyzing the six-year hourly data of power plants, power exchange, and demand.•A correlation matrix of eight input features and the three target variables.•Cross-validation improved the prediction of oversupply using random forest method. The economic viability of renewable energy is deteriorating due to its curtailment in power systems. Therefore, it is imperative to forecast curtailments for more effective utilization. To alleviate this issue, in this paper, we propose artificial intelligent-based models to accurately predict wind and solar power curtailments (WSPCs), which have not been investigated before. In this regard, a prediction methodology is developed using different types of machine learning (ML) methods and evaluated based on both hold-out (HO) and cross-validation (CV) approaches. The ML methods considered include regression trees (RT), gradient boosting trees (GBT), random forest (RF), feed-forward artificial neural networks (ANN), long short-term memory (LSTM), and support vector machines (SVR). The prediction models are trained based on eight input features, including load demands, the output power of thermal power plants, nuclear units, solar farms, wind turbines, biomass/geothermal units, large hydro units, power imports, and WSPC as two target variables. Based on historical data, i.e., hourly records of California independent system operator (ISO), the predictive models are validated, and the optimal hyperparameters are chosen using Bayesian optimization for each model to attain the best results. Among all the models, the RF model results in the minimum prediction errors and thus the best performance by implementing the proposed CV approach. The obtained results demonstrate the effectiveness of the proposed models in the prediction of WSPCs.