The paper reviews various approaches for detecting fake reviews using different machine learning techniques, each with distinct strengths and limitations. It examines existing literature on supervised learning methods, unsupervised techniques, graph-based models, and hybrid approaches. Among these, unsupervised models rely on pattern recognition, while supervised methods, including SVM and transformer-based models like BERT, offer high accuracy but struggle with class imbalance and computational efficiency. Unsupervised and graph-based models serve as effective alternatives when labeled data is scarce or when complex relationships between reviews and users must be analyzed. Additionally, hybrid approaches that integrate multiple techniques are gaining traction, as they enhance feature selection and model performance. In this paper, we explore different methodologies for fake review classification, analyze their advantages and drawbacks, and highlight key challenges in the field.