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With The Rapid Growth Of Social Networking Platforms, Facebook Has Become A Prime Target For Malicious Applications That Exploit User Trust, Harvest Personal Data, And Spread Harmful Content. These Applications Often Disguise Themselves As Legitimate Services, Making It Difficult For Users To Identify And Avoid Them. Traditional Security Mechanisms Such As User Reports And Manual Reviews Are Insufficient To Handle The Scale And Sophistication Of These Threats. To Address This Challenge, This Work Proposes A Detection Framework For Identifying Malicious Facebook Applications Using Machine Learning And Behavioral Analysis Techniques. The System Leverages Features Such As Permission Requests, Application Interaction Patterns, And User Activity Logs To Distinguish Between Benign And Malicious Applications. By Applying Classification Algorithms And Anomaly Detection Models, The Framework Can Proactively Flag Suspicious Applications Before They Cause Significant Harm. The Proposed Solution Enhances User Safety, Preserves Data Privacy, And Strengthens Trust In Social Networking Environments By Providing An Automated, Scalable, And Efficient Detection Mechanism.