The rapid growth in unstructured financial data from social media platforms, market sensors, and economic APIS has made traditional data processing frameworks incapable of performing real-time analyses. Existing frameworks are mostly limited by latency, static structure, and lack of flexibility which becomes critical in forecasting tasks such as predicting financial market crashes. This research attempts to solve these gaps by developing a unique stacking-based ensemble model which incorporates XGBoost, K-Nearest Neighbors (KNN), and Logistic Regression aimed at providing accurate, interpretable, and real-time predictions of crashes. The contribution of this research is its sophisticated mathematical framework of multi-level learners that dynamically combine the components of gradient boosting, local instance learning, and probabilistic reasoning to a centralized decision-making architecture. The model treats imbalanced classification problems using stratified sampling, grid search tuning, and probability calibration. Experimental results show the ensemble achieved 96.17% accuracy, 0.0914 log loss, and high F1 score of 96.10%, exceeding the performance of traditional single-model systems. Furthermore, the framework claimed improved reliability and reduced error margins in probability outputs confirmed by ROC curves, precision-recall graphs, and residual distribution analysis. This methodology offers a flexible and transparent ensemble model suitable for ever-changing financial scenarios that demand instant decision-making insights.