This paper presents an analytical framework designed to optimize the prediction of crude oil prices, addressing the inherent volatility and complexity of oil markets. Leveraging advanced statistical techniques and machine learning algorithms, the framework integrates historical price data, macroeconomic indicators, and geopolitical factors to enhance prediction accuracy. By employing optimization methods such as regression analysis, time-series forecasting, and artificial intelligence models, the study evaluates the effectiveness of various predictive approaches in different market conditions. Through rigorous testing and validation against real-world data, the framework demonstrates significant improvements in forecasting performance compared to traditional models. The findings provide valuable insights for investors, policymakers, and analysts, enabling more informed decision-making in the dynamic crude oil market.