This study explores the application of GARCH models for volatility forecasting in high-frequency trading (HFT) environments from 2020 to 2024. The research aims to assess the effectiveness of traditional and enhanced GARCH variants, including EGARCH and TGARCH, in capturing market volatility dynamics. A comprehensive methodology involving empirical analysis of financial market data, statistical modeling, and hybrid integration with machine learning techniques was employed. The findings indicate strong volatility persistence across years, with beta values consistently above 0.80, confirming the suitability of GARCH models for HFT markets. Model accuracy was validated using RMSE and MAE metrics, demonstrating superior predictive performance in 2021 and 2024. The study also revealed that integrating machine learning with GARCH models significantly improved forecasting accuracy, reducing RMSE by 12% on average. A correlation coefficient of 0.92 between GARCH-predicted and actual volatility further validated the robustness of these models. Despite challenges such as microstructure noise and data nonstationarity, enhancements in noise reduction techniques and real-time parameter adjustments have bolstered model effectiveness. The study concludes that while GARCH models remain fundamental tools for volatility forecasting, integrating advanced computational techniques is essential for optimizing predictive capabilities in high-frequency trading environments. Recommendations include adopting machine learning-enhanced GARCH models, implementing noise-reduction techniques, and developing real-time calibration strategies to improve forecasting precision.