In this era of booming e-commerce, online reviews carry an enormous amount of influence on the choices of consumers. This research works on distinguishing between original and spurious product review publications gathered from Amazon. The overall approach uses multiple natural language preprocessing techniques—terms, tokenization, stemming, and efficient machine learning classification algorithms like random forest, neural networks, and others. The objective is to create a correct, highly accurate model to enhance the fake review classification system. There were extreme evaluation measures, including accuracy and precision. The best performer was extreme gradient boosting, reporting an accuracy of 0.9018. It has the additional interesting implication on how one can take steps further in the improvement of performance of such a model by employing strategies such as cross-validation and adjustment of hyperparameters for robustness against overfitting. With a clear methodology and results that are very attractive, the research effort contributes to a very significant task in identifying authenticity in online product reviews.