West Texas Intermediate (WTI) crude oil price play a crucial role for the global economic stability, influencing inflation, trade balances, and financial markets. However, their inherent volatility, driven by geopolitical events and market dynamics, makes accurate forecasting a challenging task. Most of the traditional models often fail to capture the nonlinear and uncertain nature of price fluctuations. In this study, we propose a hybrid forecasting approach that integrates nonlinear autoregressive neural networks (NARNN) with intuitionistic fuzzy logic (IFL). The NARNN model effectively uses the historical price characters, while the IFL component enhances predictive accuracy by handling uncertainty and incomplete data. Our methodology is applied to historical WTI spot price data, demonstrating superior performance. The results highlight the advantages of combining neural networks with intuitionistic fuzzy logic for improved price forecasting, providing deeper insights into market trends. This approach is useful in managing risk in the volatile oil market. The findings contribute to the development of more adaptive and resilient forecasting strategies, addressing the challenges of crude oil price prediction in uncertain economic environments. A comprehensive analysis of real market conditions was made to assess the underlying dynamics and trends shaping WTI crude oil price movements and its effects to the global economy.