Prediction systems grounded in textual data have become indispensable across high-stakes domains including clinical decision support, financial signal detection, and digital misinformation analysis. Classical statistical approaches and shallow machine learning methods have demonstrated satisfactory performance on narrow, well-curated datasets, but they struggle to generalise once input distributions shift or domain vocabulary diverges from training corpora. Deep learning, and more specifically the pre-trained transformer paradigm, has substantially narrowed this gap; nevertheless, single-architecture solutions routinely leave accuracy on the table when applied to tasks that demand both rich contextual encoding and explicit sequential reasoning. This paper presents a cohesive, end-to-end AI- powered prediction framework that fuses BERT- derived contextual representations with a two-layer bidirectional LSTM (BiLSTM) classification head augmented by an additive attention mechanism. The system is designed as a modular pipeline: text acquisition and normalisation, augmentation-based imbalance handling, deep encoding, sequential modelling, and post-hoc probability calibration are treated as independent, replaceable stages. Experimental evaluation across three publicly available benchmark datasets — the LIAR fake news corpus, Stanford Sentiment Treebank v2, and a health-claim verification collection — confirms that the hybrid BERT-BiLSTM-Attention architecture outperforms five competitive baselines on macro-averaged F1 and area under the ROC curve. Ablation experiments quantify the individual contributions of the attention layer, recurrent head, augmentation strategy, and temperature scaling. A discussion of deployment trade-offs addresses inference latency, continual adaptation, and algorithmic fairness..