Time series analysis is a powerful tool in countless regions, from finance to healthcare, but is often challenged by the presence of outliers that can distort predictions and model output. This article presents a robust quantile regression-based framework for machine learning, which increases the resilience of time series analysis compared to outliers. By using quantile regression, the proposed framework captures the conditional distribution of time series data and provides a more comprehensive understanding of its underlying structure. Machine learning integration improves the ability of models to adapt to complex nonlinear patterns while simultaneously maintaining robustness to anomaly data points. Through extensive research into synthesis and practical datasets, we show that our framework outweighs traditional predictability and trigger resilience methods. The results highlight the possibility of combining quantile regression with machine learning for robust time series analysis, providing promising directions for future research and applications in the environment. Time series forecasts play a key role, especially in financial markets where accurate forecasts are useful for investors and stakeholders. However, traditional models have difficulty recording nonlinear dependencies and are not able to effectively handle outliers. This article presents a robust quantile regression-based machine learning framework for diffusion-preserving time series analysis. It integrates long-term time memory (LSTM), LightGBM, and stacked ensemble models. The proposed ensemble approach uses quantile regression for robust outsourcing processing while combining deep learning strengths to increase base techniques to improve predictive performance. Experimental evaluation of Goldman Sachs BDC, Inc.(GSBD) shared course data demonstrates the advantages of the ensemble model compared to the individual model. The results show that the stacked ensemble model reaches the lowest flipper loss (0.0656), MAE (0.1313), RMSE (0.2185), and the highest R² (0.9778), exceeding LSTM and LightGBM. The results highlight the effectiveness of hybrid ensemble learning in financial series forecasting, providing a more accurate and robust approach to dealing with outlier sacrificial data.