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The development and widespread use of social media networks, along with the expansion of the Internet, have revolutionized how news is produced and shared. Social media has updated accessibility, speed, and affordability of news, but it has also created substantial issues, including the spread of fraudulent content like fake news and phone identities. The multidimensional topic of fake news and false identity detection on online social networks is explored in this study, with an emphasis on the most recent developments in Artificial Intelligence (AI) remedies to address this issue.Concerns have been voiced by a variety of businesses and organizations about the exponential proliferation of incorrect information on social media platforms, which has lowered public confidence in the media. The need for effective online content authentication has increased due to the erosion in confidence. In the first piece of study, the goal is to use data mining techniques to find fake accounts on social networking sites. The study introduces the 3PS (Publicly Privacy Protected System) technique, which locates these rogue accounts by examining user interactions and behaviors like shared posts and recent activities. The tactic entails looking at a variety of actions, including frequent behaviors, recent updates, postings, comments, and photos. Malicious people are located by comparing attribute control limits for user profiles and analyzing network commonalities. To identify rogue accounts, feature reduction techniques are used, and the E SVM-NN classifier is employed. The study entails setting up, running, and keeping track of accounts, looking at recent behavior, data mining, and choosing test profiles.Real-time profile Investigation and identification is another important component in the fight against counterfeit identities and fake news. In order to determine if posts and user profiles are trustworthy, the planned RTPAFDM uses PLTA and P LTA. The technology detects fake profiles with a 97 percent accuracy by calculating UPTW and HTPW, hence boosting profile security.In addition, a ground-breaking PRE-Confirmation Technique is provided to solve privacy concerns. The proposed method, which is based on the 'DPAFAD algorithm,makes use of artificial intelligence to assess, notify, and request user approval before sharing or using their information. This strategy reduces the danger of information misuse or unauthorized distribution by providing real-time user consent.Finally, the research concludes with emphasizes and the significance of AI-driven solutions in preventing the spread of misleading information and phony identities on social media. The research community is making great progress in assuring the validity of online material and protecting user privacy and security using cutting-edge methods like data mining, real-time profile Investigation, and pre-confirmation algorithms. These techniques have the potential to strengthen the public's confidence in online information sources and create a more secure digital environmentKey Words:Social Media Networks, Fake News Detection, False Identity Detection, Artificial Intelligence (AI), Data Mining Techniques, Social Networking Sites, 3PS Technique

Copyright © 2025 JITENDER KUMAR. This is an open access article distributed under the Creative Commons Attribution License.