Accurate forecasting of Crude oil price (COP) is paramount for financial markets, energy sector stakeholders, and a nation’s economy. The COP is affected by periodic, aperiodic, and sporadic factors like geopolitical events of importing and exporting countries. Therefore, accurate forecasting of COPs is imperative and challenging. Motivated by this, in this project, extensive studies are made to make point and interval forecasting of COPs employing machine learning models. Optimized deep learning models are used, and deep features are extracted. The deep features are transformed using principal component analysis (PCA). The transformed features are modelled using shallow machine learning models. Once the point forecasts are obtained, error modelling using different distributions is performed to compute the interval forecasts at different significance levels. Several alternatives are considered in optimized deep learning models, machine learning models and distribution functions to make the methodology reliable and robust. Since the machine learning models are stochastic, the simulations are repeated, and the mean values are considered from different point and interval forecasting methods for drawing reliable conclusions. Simulation results suggests that the GRU-Linear Regression model with optimized hyper-parameters provides the best in MAE and MASE. At the same time, the CNN-Huber Regressor provides the best RMSE and SMAPE than all other alternatives. .