The rise of online platforms and e-commerce has revolutionized consumer behavior, making product reviews a vital source of information for purchasing decisions. However, the prevalence of fake reviews has undermined the credibility and trustworthiness of online reviews, leading to the need for effective fake review detection systems. This research paper presents a novel approach to address this challenge by leveraging supervised machine learning techniques for the detection of fake reviews. The proposed system begins by constructing a comprehensive dataset consisting of genuine and fake reviews, along with relevant features such as review text, reviewer information, and rating patterns. These features are carefully selected to capture the distinguishing characteristics of fake reviews, including the presence of biased sentiments, unnatural language patterns, and inconsistent reviewer behavior. A supervised machine learning model, such as a support vector machine (SVM), KNN,Logistic Regression is trained on the labeled dataset to learn the complex patterns and relationships between the review features and their authenticity. The model undergoes an iterative process of feature engineering, selection, and hyperparameter tuning to optimize its performance.