With the rise of online platforms, concerns are increasing about the presence of fake user profiles, which can be exploited for malicious activities such as fraud, identity theft, and spreading misinformation. This study provides a detailed analysis of four machine learning algorithms to detect fake profiles: Support Vector Machine, Logistic Regression, Passive Aggressive, and Decision Tree. To train and evaluate these models, we first collect a broad dataset of both genuine and fake user profiles. Through feature engineering, relevant data such as text content, account creation details, and behavioral patterns are extracted from the profiles. Support Vector Machine is selected for its capacity to manage high-dimensional data and reduce the risk of overfitting, while Logistic Regression is valued for its interpretability and capability to model complex relationships. Passive Aggressive is included to test performance in real-time scenarios, where fake profile characteristics may evolve due to its adaptability to changing data streams. Decision Trees are employed for their ability to capture non-linear relationships and offer insights into the decision-making process. Metrics like recall, accuracy, and precision are used to evaluate the performance of each algorithm. This comparative analysis enhances our understanding of machine learning approaches for detecting fake profiles and offers practical insights for developers aiming to mitigate risks associated with online fraud. Among the algorithms, Decision Tree achieved the highest accuracy at 98.76%.