Due to the exponential growth of internet content and the widespread presence of social media platforms, the proliferation of clickbait headlines has become a critical issue in the digital age. Clickbait undermines the credibility of online content and misleads users by presenting sensationalized or deceptive headlines that often fail to deliver relevant information. Therefore, detecting clickbait is essential not only to maintain the trustworthiness of information but also to ensure that users receive meaningful and accurate content. This paper presents a comprehensive review of various machine learning (ML) techniques employed for clickbait detection. Among the discussed models are cutting-edge methods such as BERT-based transformers, along with classical algorithms like Naive Bayes, Generalized Linear Models (GLM), Logistic Regression, Latent Dirichlet Allocation (LDA), Multilayer Perceptron (MLP), and Random Forest classifiers. Each approach is evaluated in terms of its underlying methodology, highlighting specific strengths that make it effective for identifying misleading headlines. The paper further analyzes key aspects such as feature engineering, model complexity, training data size, and the ability of models to generalize across diverse content sources. A comparative performance analysis is conducted using standard metrics such as precision, recall, F1-score, and accuracy, offering valuable insights into the relative effectiveness of each method. The review concludes by emphasizing the potential of hybrid models that integrate the complementary advantages of multiple algorithms to build more robust and scalable systems. Future work is expected to focus on deep learning integrations, real-time detection systems, and enhanced quantitative analysis.