Our research delves into a comprehensive array of preprocessing techniques, harnessing the power of BERT, TF-IDF, and Word to Vector methods to refine feature representations. Leveraging a diverse ensemble of Deep Learning and Machine Learning models, including CNN and its variants, XGBoost, LightGBM, AdaBoost, SVM, and others, we embarked on extensive experimentation across two distinct datasets. In a groundbreaking stride, we introduced an innovative adaptive sliding window model seamlessly integrated with drift detection mechanisms. This proposed model is meticulously crafted to autonomously elevate accuracy by meticulously retraining machine learning models at judiciously determined intervals, dynamically adapting to the ever-evolving patterns inherent in streaming data. The synergistic amalgamation of advanced preprocessing techniques, a diverse portfolio of models, and an adaptive sliding window with drift detection mechanisms propels our research to the forefront of optimizing predictive accuracy in dynamic data environments.