With the increasing reliance on online reviews across various digital platforms, user feedback has become a vital factor in influencing public perception and decision-making. Since users cannot physically verify products or services online, they often depend on reviews to assess quality and credibility. This dependency has led to a rise in deceptive practices, where fake reviews are used to mislead audiences—either by promoting certain offerings or undermining competitors. Detecting such fraudulent content presents a significant challenge in the field of natural language processing (NLP), due to the subtle and human-like nature of these reviews. In this project, we present an approach for fake review detection using a deep learning model that combines Long Short-Term Memory (LSTM) networks with Bidirectional Encoder Representations from Transformers (BERT). Our model utilizes LSTM’s ability to capture long-range dependencies along with BERT’s contextual language understanding to enhance detection accuracy. To improve practicality and trustworthiness, we incorporate several additional features plugin support for easy integration into various review-based platforms, multilingual capability to handle reviews in different languages, and LIME (Local Interpretable Model-agnostic Explanations) to provide word-level interpretability of predictions. We evaluate our model on publicly available datasets containing both real and fake reviews, and the results demonstrate that our LSTM-BERT approach significantly outperforms traditional machine learning techniques. This work contributes to the growing efforts in combating misinformation and enhancing the credibility of online content across diverse platforms.