The detection of Spam Reviews is a critical challenge in today’s era of online platforms, where dishonest opinions of any product or service can manipulate consumer decisions. This research performs a comprehensive comparison of Machine Learning (ML), Deep Learning (DL), and Graph Neural Network (GNN) models to address the challenge of fake review detection. Traditional machine learning methods capture handcrafted features but often struggle with complex textual patterns. Deep learning models leverage hierarchical feature extraction and achieve superior performance by identifying deeper semantic cues. However, ML and DL models primarily treat reviews as independent entities, neglecting interaction or crucial relational information among reviews, reviewers, and products. To overcome this limitation, we explore GNN-based models, which use graph-based representations to model structural and contextual relationships across entities in review datasets. We evaluated the performance of various classifiers, highlighting the strengths of each approach on three benchmark deceptive review datasets—Hotel, Restaurant, and Doctor. Our evaluation result shows that DL models outperform traditional ML models due to their ability to discover latent semantic and sequential patterns in the data without manual feature engineering. Furthermore, GNN models leverage both node-level content and edge-level relationships to capture global structural dependencies among review entities, enhancing their ability to detect subtle and coordinated deceptive behavior. These models provide a more robust solution and highlight the necessity of integrating relational knowledge for review analysis to enhance fake review detection performance.