The emergence of counterfeit news on platforms like social media and various other online applications has become an international problem. It shapes public opinion, impacts election results, and carries the risk of affecting public health. This article articulates a system designed to identify false news using Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. The system was created to analyze news in real-time using processed news through verified trustworthy APIs; a data cleaning paradigm; feature extraction of critical features; machine learning (ML) and deep learning (DL) classification methods; and using a credibility scoring process to account for uncertainty in news classification. We illustrate the processes of selecting, engineering and extending datasets; feature engineering; modeling (traditional and transformer); training paradigms; and hybridizing instances that support fast inference in two coding languages, Java and Python. Our efforts on extensive experimentation on publicly available datasets and real-time API streams indicate that the system provides a comparable accurate output with interpretability scores and practical throughput measures. Also of report are the limitations of error; discussion of available methods for ethical and privacy-aware deployment of the method of use; and limitations giving suggestions or pathways for future work. Finally, we offer a pathway for continued work that is multilingual and/or multimodal...
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