The spread of online social media platforms in the digital age has drastically changed how people receive and distribute information. But this change has also brought about the rise of dishonest tactics like clickbait and bogus reviews, which compromise the authenticity and dependability of internet material. This review article offers a thorough analysis of the approaches and technology developments used today to identify clickbait and fraudulent reviews on social media websites. We examine several machine learning and natural language processing techniques used for detection, analyze the underlying motivations for these deceptive acts, and talk about the effectiveness of these approaches in diverse social media environments. We also draw attention to the difficulties and constraints that practitioners and academics in this field must overcome, such as problems with algorithmic bias, data quality, and the dynamic nature of misleading strategies. This work attempts to contribute to the creation of more robust and successful tactics for countering online deception, ultimately improving the credibility of social media ecosystems, by synthesizing recent research findings and highlighting important trends.