The crude oil price (COP) has substantial implications on world economy, as it impacts industries ranging from transportation to manufacturing. Given the volatile nature of COP, accurate forecasting is very much crucial for businesses and policymakers alike. Forecasting crude oil prices is a challenging task for the complex and volatile nature of the global oil market. As a result, estimating the price of crude oil has been a challenging and crucial component of forecasting research. In this study, we employ fourteen machine learning (ML) models for predicting the weekly and daily crude oil price. To evaluate the effectiveness of ML models, four performance measure metrics are utilized, including “mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root mean square error (RMSE), and mean absolute error (MAE)”. Detailed statistical analyses of data obtained using the Wilcoxon Signed-Rank test demonstrate that the linear support vector regression (SVR) model for weekly COP data, and linear regression for daily COP data are statistically more effective in predicting COPs than other models considered. The linear regression model acquires the statistically best rank across three accuracy metrics (SMAPE, MAE, MASE) and Gradient Boosting acquires the best rank based on RMSE accuracy metrics considering both weekly and daily COP data according to the Friedman and Nemenyi hypothesis test.