This study investigates the enhancement of noise detection in high-frequency ETH/USD trading data by integrating Kalman filter with various machine learning models, optimized using Optuna. The research compares a standalone Kalman filter approach to hybrid models that incorporate machine learning, including Artificial Neural Networks (ANN), Light Gradient Boosting Machines (LightGBM), Random Forests (RF), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Bidirectional LSTM (BiLSTM), and Isolation Forests (IF). The models are evaluated using noise reduction metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Noise Reduction Ratio (NRR), alongside statistical tests including paired t-tests, Wilcoxon Signed-Rank tests, and ANOVA. Key findings demonstrate that the machine learning-augmented Kalman filters, particularly those using ANN and LightGBM, significantly outperform the traditional Kalman filter in terms of noise suppression. These models achieved lower MSE and higher SNR, indicating better preservation of actionable market signals while reducing irrelevant noise. The statistical tests validate the reliability and effectiveness of these improvements. Although deep learning models like BiLSTM show promise, further optimization is required to match the effectiveness of the leading models. This study contributes to the field of financial time series analysis by demonstrating the potential of combining classical filtering methods with modern machine learning techniques. The results suggest that such integrations can lead to more adaptive and effective noise detection and detection and reduction strategies, potentially improving trading systems in volatile market environments.