The rise of online reviews has significantly influenced consumer purchasing decisions, but it has also led to an increase in fraudulent reviews that can artificially boost or tarnish a business's reputation, particularly affecting small businesses. To combat this, we propose a novel pipeline for fake review detection that integrates text data, handcrafted features, and rating-category features to enhance robustness. Our pipeline includes innovative components such as a Context Aware Preprocessor, Fake Review Feature Adder, Category Rating Embedding, Tokenizer Padding, Adaptive Fusion Layer, and Trust Aware Berta Classifier. Applied to the Amazon dataset, our BERT model with the Adaptive Fusion Layer achieves an AUC-ROC score of 0.96, demonstrating its effectiveness in detecting fake reviews. This research underscores the potential of advanced NLP techniques to maintain the authenticity of online reviews, thereby protecting both businesses and consumers from the negative impacts of fraudulent reviews.