Due to the exponential growth of data, there is a demand for knowledge that can be utilized derived from massive and massively varied datasets in diverse domains. This paper presents an Optimized Hybrid Framework which combines benefits of data mining techniques and contemporary machine learning algorithms to improve efficiency, effectiveness of information extraction. The methodology is based on two hybrid ensembles of unsupervised learning system. a combination of the Supervised Learning algorithm: Logistig regression; K-nearest Neighbors; Decision tree; Random Forest; Ad boosting; Ligh Gradient Boosting Machine and eXtreme Gradient Boosting. The former is developed with traditional classifiers, while the latter is constructed by gradient boosted automatic learning models. These normalization, feature scaling and encoding are parts of the general preparation pipeline that assures that you correctly prepare your data. Cross-validation and hyperparameter tuning are used to speed up training, avoid its overfitting, aid generalization, and enhance a model’s performance. Standard measures, accuracy, precision, recall, F1 score as well as AUC-ROC are employed to assess the performance and computational efficiency. Experimental Results The experiments show that the hybrid models can significantly improve classification accuracy as well as robustness compared to single classifiers on many datasets. At the single model level, accuracies went from 82.5% (KNN) to 92.1% (XGBoost) and AUC-ROC scores between 0.84 and 0.94. However, the hybrid ensembles reported better metrics: Hybrid 1 (LR + KNN + DT + RF) achieved 93.4% accuracy and 0.95 AUC-ROC while Hybrid 2 (AdaBoost + LightGBM + XGBoost) yielded an accuracy of 94.7% with an AUC-ROC value of 0.97. These findings validate those aggregating multiple classifiers enhances the accuracy and generalization. It is also interpretable and scalable, which means that the framework can be used for other similar knowledge discovery tasks in healthcare industry, finance industry, cybersecurity and so on. This might thus make it possible to generate building blocks for advanced intelligent decision-support systems, and supply a found able, modular and performance driven approach for knowledge extraction in complex data environments.