In recent eras, the complexity and fluctuations of the global crude oil prices have affected the economic progress of society. It is therefore, the oil price prediction has hauled the attention of scholars and policymakers. Driven by this critical concern for forecasting of crude oil prices, we introduces a novel hybrid model keeping in mind the primary objective of enhancing prediction accuracy while considering the specific characteristics as inherent in the data. To achieve this achievement, the trend is eliminated, allowing the scrutiny of whether the residual component validates the assurance of a series ran by stochastic trends. Following the removal of the trend, the residual component undergoes rigorous evaluation through autoregressive model following the decomposition model. Then we got support from the support vector machine, autoregressive integrated moving average and long-short term memory. The predictions accuracy can be evaluated by using the various performance metrics. The proposed hybrid model’s robustness and forecasting performance are rigorously evaluated through Diebold-Mariano test in comparison to competing models. Furthermore, the forecasting ability is evaluated via directional forecast. Ultimately, the empirical findings explicitly determine the superior predictive capabilities of the proposed hybrid model over alternative approaches.